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Speaker 1: Welcome to Dope Labs, a weekly podcast that mixes hardcore science,

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Speaker 1: pop culture, and a healthy dosa friendship. This week, we

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Speaker 1: have a special episode that was recorded live at the

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Speaker 1: Great Northern Festival in Minneapolis, Minnesota. We had so much

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Speaker 1: fun during this live show because we talked about the

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Speaker 1: science of all things winter and what better place to

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Speaker 1: talk about the science of cold and all things winter

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Speaker 1: than the coldest place that I have ever been to

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Speaker 1: in these United States of America.

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Speaker 2: And we got a lot of flak tt on Instagram.

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Speaker 2: People were saying, why didn't you tell us you were

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Speaker 2: having a live show?

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Speaker 1: Well, y'all are spicy spicy in the comments, but we're

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Speaker 1: gonna go ahead and tell people.

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Speaker 3: Now.

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Speaker 2: We have a live show on April fourteenth at seven

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Speaker 2: pm at the Boston Museum of Science. So if you're

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Speaker 2: in the greater Boston area, come out and join us.

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Speaker 3: Tickets are free.

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Speaker 2: All you have to do is register and you can

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Speaker 2: get this same energy on April fourteenth. Yes, there will

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Speaker 2: be a link in our show notes for you to

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Speaker 2: register and come again. Those tickets are zero dollars and

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Speaker 2: zero cents. So we want to see you.

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Speaker 3: All right, Let's get into it.

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Speaker 2: Here's our conversation, recorded live from the Great Northern Festival.

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Speaker 4: M I came out too hot, too hot too. You

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Speaker 4: threw your micro my mic back flew off.

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Speaker 3: How are y'all feeling?

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Speaker 4: Thank you all so much for being here? It is cold.

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Speaker 4: I was like I wouldn't come.

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Speaker 3: But we're glad that you be very glad.

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Speaker 2: All right, so we like to have a little you know,

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Speaker 2: I don't know anybody here. Has anybody heard Dope Labs before?

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Speaker 3: Some people?

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Speaker 4: Okay, okay, so.

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Speaker 3: Y'all in the lab, all right, all.

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Speaker 2: Right, ok so you already know it's gonna be some

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Speaker 2: cackling in here. We're going to be laughing a lot tonight.

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Speaker 2: For those of you that don't know us. I'm Tt

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Speaker 2: and I'm Zachiah, and we both host Dope Labs, which

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Speaker 2: is our podcast that's for people who like science and

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Speaker 2: for people who are just like I'm not going okay,

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Speaker 2: I'm not quite sure. So we want to have a

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Speaker 2: little bit of fun with y'all. For just a little backstory.

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Speaker 2: Our podcast is built on like our experience as grad students.

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Speaker 2: So I'm a molecular biologist slash geneticist.

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Speaker 3: You know, I've done a little bit.

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Speaker 2: Of biochemistry, all of that, right, And when I was

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Speaker 2: in grad school, I met TT.

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Speaker 1: Yes, and I was starting grad school and I was

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Speaker 1: I'm in material science and engineering, mechanical engineering, a little

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Speaker 1: bit of chemistry, a little bit of bio materials.

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Speaker 4: But not really. I'm not going to pretend like I

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Speaker 4: know any biology.

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Speaker 2: And so we liked for our podcast to feel like

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Speaker 2: what we think our experience was, which was friendship and science,

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Speaker 2: but not like.

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Speaker 3: Dry eyed science, right, a little bit of exciting science.

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Speaker 2: And so we graduated, and this is maybe twenty fourteen,

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Speaker 2: twenty fifteen, getting on up into the twenty sixteen and seventeen.

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Speaker 2: Anytime we told a joke, we said something that everybody

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Speaker 2: else was saying.

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Speaker 3: We should have a podcast, right, if you left.

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Speaker 4: A joke, Yeah, isn't that what everybody's doing. Isn't that

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Speaker 4: how it goes?

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Speaker 2: So that's kind of where we started, and we just

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Speaker 2: wanted to bring this, you know, care for each other,

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Speaker 2: this passion for science and a passion for explaining science.

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Speaker 2: So I like to say, well, TT says it most

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Speaker 2: of the time that I never meet a stranger, Okay, never,

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Speaker 2: I'm Southern. That's how it works. And you know, we

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Speaker 2: would go out and talk.

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Speaker 4: To people and we said she was or most of

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Speaker 4: the talking.

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Speaker 1: I was just doing the listening and like, hey, this

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Speaker 1: person may kill us.

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Speaker 3: No needs a fear. I'm good in hand to hand combat.

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Speaker 2: But if people found out that we were scientists, they

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Speaker 2: would ask questions like, you know, uh, I never figured

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Speaker 2: I never understood, like why is the sky blued? People

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Speaker 2: say it's something you know that's really simple, but can

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Speaker 2: you explain it?

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Speaker 1: Or they would ask, I don't understand about buoyancy? How

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Speaker 1: do ships stay afloat? Or they would ask things that

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Speaker 1: were more biological, biological in its background, where Zekiir would

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Speaker 1: really be able to chime in and I would just

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Speaker 1: be listening and you know, sipping my tea.

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Speaker 2: But I think what we found is this is where

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Speaker 2: we really loved science, at that intersection of excitement and

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Speaker 2: question asking and accessibility. And so that's what we try

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Speaker 2: to do with Dope Labs, and that's what we're gonna

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Speaker 2: try to do with y'all tonight.

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Speaker 3: Okay, also, buckle up, buckleloup, buckle up. All right.

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Speaker 2: So when we were saying, like okay, first of all,

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Speaker 2: girl is gonna be cold.

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Speaker 1: It's gonna be cold, not ready. Those are the two

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Speaker 1: things that we knew. It's gonna be cold and we're

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Speaker 1: not ready.

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Speaker 2: I'm gonna tell one of t T secrets since she's

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Speaker 2: gonna be upset. Maybe she wanted to wear a snowsuit

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Speaker 2: for this.

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Speaker 4: It was for fashion's sake.

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Speaker 1: I thought you a would really appreciate it. You understand

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Speaker 1: you and say, listen, I see you, I know what

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Speaker 1: you're doing.

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Speaker 4: It didn't come in time.

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Speaker 2: But you know, I said, hmm, that could be a

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Speaker 2: good end. You know, when we're talking about science, there's

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Speaker 2: just so much we could start with. When we think

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Speaker 2: about pop culture and what's happening, I said, what will

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Speaker 2: people want to hear from us? Maybe we could talk

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Speaker 2: about the lunar New Year that's happening.

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Speaker 4: Maybe yeah.

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Speaker 2: And then I said, maybe we could be spicy and

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Speaker 2: talk about Joe Rogan.

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Speaker 3: Maybe maybe.

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Speaker 2: But then I said, we should really talk about what's

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Speaker 2: really happening today, and the people aren't really I haven't

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Speaker 2: seen too much on Twitter Instagram about it, but it's

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Speaker 2: groundhouse back.

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Speaker 3: Yes.

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Speaker 1: Now, I grew up in Maryland, so that's just south

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Speaker 1: of Pennsylvania and punks and tiny phil This isn't him

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Speaker 1: I know him when I see him. Every Groundhog Day,

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Speaker 1: it's like, well, at least in Pennsylvania, it's a holiday,

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Speaker 1: and everybody's waiting to see if the groundhog sees it shadow,

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Speaker 1: and we're keeping our fingers crossed that he doesn't so

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Speaker 1: that we don't have to experience anymore cold winters. But

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Speaker 1: it turns out this morning he did. I think you

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Speaker 1: guys didn't really care because but I've read on the

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Speaker 1: East coast is not very happy about it.

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Speaker 2: And so when we started thinking about, you know, groundhogs, now,

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Speaker 2: TT has way more experience than I do with the groundhog,

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Speaker 2: like go way back. Yeah, not so much in North Carolina,

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Speaker 2: we don't really check in with that. But I was like,

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Speaker 2: is there a science to it? Not really, I feel

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Speaker 2: like this is just, you know, people are just having

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Speaker 2: a good time, having a good time. But then we

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Speaker 2: did start thinking, like, how do we know about what's

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Speaker 2: going to happen severe weather events and change in climate.

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Speaker 2: I think that's a big theme of the Great Northern

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Speaker 2: And so, you know, we have been We've had a

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Speaker 2: couple of episodes some of you may have heard them.

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Speaker 2: We've talked about some of the fires that we've seen

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Speaker 2: on the West Coast that we've seen in Australia, and

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Speaker 2: just you know, I think trying to get that sweet

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Speaker 2: balance when we're talking to folks about the difference between

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Speaker 2: weather day to day, like today supposed to feel like

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Speaker 2: minus fifteen okay, and people, you know, if I share

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Speaker 2: that on Instagram, people are gonna say, like, oh, that

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Speaker 2: just shows you climate change. It's not really that warm.

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Speaker 2: Look how cold it is. And I'm like, no, no, no, no, no,

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Speaker 2: We're talking about climate that's over a long period of time, right,

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Speaker 2: And so we're seeing all of these severe weather events

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Speaker 2: tied to increasing temperatures, ocean acidification, seeing melting of snow,

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Speaker 2: and so we're seeing a lot more things that feel

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Speaker 2: like heat waves, seeing way more heat waves. We're seeing

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Speaker 2: you know, hurricanes, way more hurricane activity than I ever remember,

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Speaker 2: now I have to have many years of record.

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Speaker 3: I don't have many years of record.

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Speaker 2: But we're seeing way more of this stuff, and that

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Speaker 2: still feels like it's related to winter. You know, it's

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Speaker 2: still related to all these things are kind of tied together.

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Speaker 3: And so I think.

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Speaker 2: We're starting to see a change of people coming around

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Speaker 2: the corner for kind of seeing that we all are

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Speaker 2: tied together, like these borders of what's Minnesota, what's Wisconsin,

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Speaker 2: What's I.

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Speaker 3: Don't even know if those are right beside each other.

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Speaker 4: Don't We didn't say we need geography?

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Speaker 3: Yeah, yeah, yeah, I mean it's a science, but it's

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Speaker 3: not mine.

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Speaker 2: And you know, and so you know that's that's really

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Speaker 2: like fake kind of I mean, it's artificial, right, because

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Speaker 2: what's happening on the East Coast, what's happening in the Midwest,

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Speaker 2: all of that is all tied together. What's happening in

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Speaker 2: the North Pole, in Antarctica, in the Pacific Ocean. You know,

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Speaker 2: we just saw a volcano eruption there. All of those

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Speaker 2: things are tied together. And that's because this is.

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Speaker 3: All the water that we have.

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Speaker 2: Now, I don't know what y'all learned in elementary school,

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Speaker 2: maybe about the water cycle. Anybody have a water cycle

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Speaker 2: song that they know? No? Oh, I see somebody saying,

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Speaker 2: maybe it's a spotlight you're saying no, now, no, I

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Speaker 2: promise you. I mean I thought we were having a

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Speaker 2: good time, and so I had a water cycle song

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Speaker 2: and it was like condensation, evaporation, precipitation on my mind.

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Speaker 3: Anybody heard that.

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Speaker 4: I've never heard that that's hurtful.

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Speaker 2: Well, it's all part of the water cycle and it

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Speaker 2: happens all the time.

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Speaker 3: That was that second verse. But when we think about that,

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Speaker 3: this is all the water that we have.

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Speaker 2: So what's happening in Australia run off on the roads

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Speaker 2: and into the rivers and into greater bodies of water,

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Speaker 2: you know, into different basins. That's the same water from

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Speaker 2: that's ancestral water, right, Like that's water that evaporates your grandma's,

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Speaker 2: mama's water, you know. And when you really think about that,

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Speaker 2: that water has a record, like that water has a memory,

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Speaker 2: and we have to take care of it. One of

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Speaker 2: the things we've been seeing is over time, right when

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Speaker 2: we look at when we think about climate and we

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Speaker 2: think about changes on a really large scale over time,

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Speaker 2: we're seeing some warming oceans.

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Speaker 3: And you know, it may feel good for a vacation.

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Speaker 3: You're like, oh it's warm.

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Speaker 2: Out here, you know, Oh give my you know, get

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Speaker 2: my selfie, get my pictures off, right, But that's not

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Speaker 2: the only thing that's happening with these oceans. What we're

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Speaker 2: also getting is what I like to call spicier oceans. Okay, Now,

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Speaker 2: anybody suffer from like acid reflux or heartburn. Y'all don't

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Speaker 2: have that either. So y'all didn't know the water cycle

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Speaker 2: and you don't have heartburn. Okay, I'm just keeping I'm kidding, Store,

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Speaker 2: it's just me. It's well if i'm if I'm trying

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Speaker 2: to have a good time, I'm definitely having a roll, lads. Okay,

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Speaker 2: the ocean is is acidifying. Anybody heard of that before? Okay,

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Speaker 2: all right, we got to inform crowds. So when we

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Speaker 2: think about what does it mean to acidify? We know

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Speaker 2: about the pH scale. Have y'all seen alkaline water? Okay,

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Speaker 2: they say, like that's pH nine and above. So if

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Speaker 2: you look at this scale up here over in the blues,

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Speaker 2: that's your alkaline. What else is in the blue bleach

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Speaker 2: is alkaline? I'm trying to think soap is alkaline? The

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Speaker 2: ocean should be neutral. We wanted to be around seven.

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Speaker 3: Okay.

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Speaker 2: When you start to get over into the acidic areas,

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Speaker 2: you get into like lemon juice and tomatoes and coffee.

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Speaker 2: Those are the things that bother me. And then stomach acid,

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Speaker 2: which is around like pH two. Now our oceans are

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Speaker 2: because of the activity that we're having here as inhabitants

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Speaker 2: of planet Earth. Our oceans are starting to capture a

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Speaker 2: little bit more carbon dioxide, and we're getting this intricate process.

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Speaker 2: I won't take you through the chemistry, but if you

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Speaker 2: ask me later, I will. And we're getting some ocean asidification.

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Speaker 2: And people are like, oh, don't be so alarmed. The

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Speaker 2: pH is only changing, Like we're looking at steps of

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Speaker 2: one here on this scale behind us, But the pH

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Speaker 2: is only changing by zero point like point one, So

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Speaker 2: that's only a little bit. And I said, my friends, okay,

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Speaker 2: you're missing the point. It's a log scale. And so

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Speaker 2: if you guys have seen you know, not to take

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Speaker 2: us back to the past few years and what's happening.

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Speaker 3: But we've seen log scale before.

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Speaker 2: And so that zero point one percent change or zero

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Speaker 2: point one step change is about a thirty percent change

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Speaker 2: in acidification. So we're getting way more acidic. I don't

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Speaker 2: want to swim in a gin and tonic like, I

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Speaker 2: don't want the ocean to feel like that.

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Speaker 3: And it's not just me.

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Speaker 2: If you won't do it for yourselves, if we won't

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Speaker 2: change our behavior and stuff for you know, the future generations,

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Speaker 2: at least do it for the delicacies that we love. Okay,

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Speaker 2: So what we know with this like ocean acidification, is

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Speaker 2: that we're losing basically our gems, our treats, our oysters

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Speaker 2: and other organisms that create shells and exoskeletons, crustaceans, anybody

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Speaker 2: like shrimp.

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Speaker 3: I'm gonna stop asking.

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Speaker 4: The thank you them.

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Speaker 2: They don't eat anything, They don't eat anything, they don't

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Speaker 2: have heartburn, and they.

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Speaker 3: Don't know about the water cycles.

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Speaker 4: Just us.

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Speaker 2: Okay, So you guys do know some crustaceans and you

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Speaker 2: care about those. And so we're thinking about, you know,

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Speaker 2: when we look at the projection for what the pH

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Speaker 2: of the ocean might be based on the changes that

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Speaker 2: we're seeing now, we're thinking at the end of the century.

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Speaker 3: So twenty one hundred.

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Speaker 2: It's wild to say that when they've done scientists have

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Speaker 2: done experiments and they're saying, like snails and things that

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Speaker 2: create shells that are in the ocean, the shells are

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Speaker 2: dissolving like within forty five minutes because of the acidification

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Speaker 2: that we expect to have.

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Speaker 3: So I'm like, we got to make some.

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Speaker 1: Changes, absolutely absolutely, And one of the major changes that

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Speaker 1: we have to also think about when it comes to

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Speaker 1: these delicacies that are in the ocean. That we're thinking

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Speaker 1: about is microplastics. I think all of us are aware

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Speaker 1: that our ocean has a huge plastic problem. And plastic

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Speaker 1: is a really interesting material. I'm a material scientist. So

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Speaker 1: we'll talk about it just a little bit and we'll.

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Speaker 4: Keep going plastic. There's no way for it to degrade.

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Speaker 1: The plastic that you see in everything that we have

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Speaker 1: in our clothes, that we have in our shoes, that

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Speaker 1: we have in pretty much every.

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Speaker 4: Corner of our rooms. That plastic will exist forever.

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Speaker 1: It will never degrade, and it has to go somewhere,

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Speaker 1: and one of the places that is going is into

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Speaker 1: the ocean. Right now, they're saying that a majority of

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Speaker 1: the microplastics that are found in the ocean are from textiles,

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Speaker 1: so like clothing and things like that, because apparently y'all

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Speaker 1: are throwing your clothesway in the trash.

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Speaker 2: I didn't know people were doing that. I didn't know

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Speaker 2: I had plastic clothes. Content is the fabric of my life.

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Speaker 2: Car tires, Okay, I thought that was rubber material scientists,

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Speaker 2: I defer.

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Speaker 4: City dust exactly.

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Speaker 2: I mean, if you've been in New York, you know

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Speaker 2: what city does this, you're basically chewing the air.

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Speaker 4: It's nasty.

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Speaker 1: And then also what's really interesting is another large part

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Speaker 1: of it is road markings. Road markets are actually thermoplastic.

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Speaker 1: So I don't know if you've ever seen some road

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Speaker 1: markings being put down, but like they'll put fire on

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Speaker 1: top of it and helps it adhere to the road.

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Speaker 1: So it has synthetic materials as resin, and it's not

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Speaker 1: just paint. And so when it rains, when it snows

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Speaker 1: and it runs into our sewers, that's where it gets

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Speaker 1: incorporated into our water system and then that eventually gets

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Speaker 1: out into the ocean.

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Speaker 4: So there have been a lot of.

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Speaker 1: Studies that have been done to figure out how much

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Speaker 1: plastic is there actually in the ocean, and so you know,

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Speaker 1: let's show them. So there was some research that was

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Speaker 1: done with salps and they over eight years, they looked

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Speaker 1: at the gut of over one hundred salps and they

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Speaker 1: found microplastics in all of their stomach. So the reason

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Speaker 1: why they chose this type of fish, I had never

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Speaker 1: even seen this before. It looks like that, yes, is

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Speaker 1: because it's a filterfish. They live in about a mile

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Speaker 1: deep in the ocean and to propel themselves through the ocean.

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Speaker 1: They take water in and then they push it out,

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Speaker 1: and that's also how they eat. So then they became

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Speaker 1: like a really great animal to look at to figure

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Speaker 1: out if there's microplastics. They found microplastics in all one

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Speaker 1: hundred over eight years. So they are estimating that in

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Speaker 1: the ocean that there is about eight million microplastic particles

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Speaker 1: per cubic meter.

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Speaker 3: I thought you meant total. We're gonna say total, no.

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Speaker 4: Cubic meter, eight million microplastics. So it's a problem. It's

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Speaker 4: a really really big problem.

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Speaker 1: And so like I was saying, when you have we

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Speaker 1: have all of these ways for the microplastics to get

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Speaker 1: into our water system, into our ocean, and like Zekiah

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Speaker 1: was saying, this water is all that we have, and

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Speaker 1: it has that closed loop, so it's really cyclical. You know,

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Speaker 1: it rains, it goes into the water, evaporates, goes back

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Speaker 1: into the sky.

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Speaker 4: We drink it.

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Speaker 1: Now they're finding microplastics in snowpack in mountains, they're taking samples.

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Speaker 1: This is a paper from Europe, but they've also found

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Speaker 1: it in Colorado. So we know that there's microplastics in

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Speaker 1: the snow. And so then you think about the animals

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Speaker 1: that live there, the people who drink water that's really fresh,

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Speaker 1: that it comes from the mountains, and what does that

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Speaker 1: mean for us as humans and as how we consume.

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Speaker 1: So when you think about snow and microplastics in the

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Speaker 1: snow and snow formation, it seems really possible that microplastic

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Speaker 1: could be in the snow that's just falling down onto

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Speaker 1: our streets.

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Speaker 4: Because if you think of the structure of water, so

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Speaker 4: h two oz.

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Speaker 1: When it's frozen, it makes us really discrete hexagonal pattern,

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Speaker 1: and so as a snowflake grows, it's going to expand

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Speaker 1: in a way that only that fits that hexagonal pattern,

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Speaker 1: which is why snowflakes are always pretty symmetric.

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Speaker 4: But they also are never the same, as we all know.

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Speaker 1: And the reason why they're never the same is because

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Speaker 1: up in the clouds when you have the water vapor.

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Speaker 1: So some people think that snowflakes start from a water droplet,

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Speaker 1: that's not true.

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Speaker 4: It's water vapor.

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Speaker 1: That water vapor then condenses onto a piece of dust

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Speaker 1: or a piece of plastic, and that's when it starts

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Speaker 1: to grow. So if you go back one side, you

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Speaker 1: can see that dependent on depending on the humidity. So

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Speaker 1: the amount of water vapor that is in the cloud,

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Speaker 1: and then the temperature at the time that dictates the

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Speaker 1: size and shape of your snowflake. So the colder it is,

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Speaker 1: the more it's the snowflake stays pretty flat.

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Speaker 4: The warmer it gets.

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Speaker 1: No, the colder it is, the more intricate that the

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Speaker 1: design gets. The warm it is, the more it stays flat,

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Speaker 1: the more humidities, the more water vapor. You could see

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Speaker 1: that it becomes more and more and more intricate. And

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Speaker 1: these snowflakes aren't the same because while they're in the

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Speaker 1: their water vapor state and they're beginning to grow, they're

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Speaker 1: traveling through these clouds, they're flying all over the place,

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Speaker 1: and no two snowflakes take the same path.

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Speaker 4: It's kind of like people.

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Speaker 1: And so by the time they reach their critical mass

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Speaker 1: and they start to get too heavy to stay inside

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Speaker 1: of clouds, that's when they start to fall down.

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Speaker 4: And so when you start to.

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Speaker 1: Think about snow and microplastics, it really makes you think

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Speaker 1: about how you know, when we were kids, we were

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Speaker 1: outside and the snowing and you stick it hung up.

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Speaker 1: Now it kind of it kind of ruins that for

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Speaker 1: everybody because you don't want to do that too much.

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Speaker 1: I don't think anymore, because then you might end up

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Speaker 1: pooping legos.

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Speaker 4: We're gonna take a quick.

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Speaker 1: Break, but when we get back we'll have more from

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Speaker 1: our live show at the Great Northern Festival. We're back,

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Speaker 1: but before we get to today's show, just the heads

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Speaker 1: up that next week we have a really great episode

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Speaker 1: for you. It's all about the connection between our minds

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Speaker 1: and our bodies. We're talking to doctor Suzanne O'Sullivan about

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Speaker 1: psychosomatic disorders.

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Speaker 2: So don't miss it. Now let's get back to the

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Speaker 2: Great Northern Festivals. I'm not up for pooping legos, but

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Speaker 2: I think the thing we have realized. So we've been

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Speaker 2: here for a couple of days, and I think the

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Speaker 2: major takeaway I've been having a good time.

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Speaker 3: I've been having a great time here. Okay. I came

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Speaker 3: here from Atlanta.

408
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Speaker 2: I put on I bundled up, and I said, Wow,

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Speaker 2: winter is different here, Like y'all are doing stuff.

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Speaker 4: No one's staying at home, everybody's out.

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Speaker 2: These winters are not created equal. Now winter for me,

412
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Speaker 2: I'm like, oh, dreary, there's nothing to do. Everything's closed.

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Speaker 3: Uh, there's no snow.

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Speaker 2: And even the call, even the thought that there might

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Speaker 2: be snow. Uh, there's way less activity than we see here.

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Speaker 2: And so, you know, I just have been like, we

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Speaker 2: could could learn something, We could learn something, you know,

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Speaker 2: maybe we could maybe I need to be here in

419
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Speaker 2: the winter, like to see all the things that are

420
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Speaker 2: going on with the Great Northern I don't know if

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Speaker 2: you guys have done anything else that's part of the festival.

422
00:21:50,800 --> 00:21:54,960
Speaker 2: I mean, you have pond hockey. I see people doing

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Speaker 2: some things. We're gonnahow you a little bit because I

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Speaker 2: have some questions for y'all. You know, people doing all

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Speaker 2: kinds of stuff just outside, having a good time, brewery tours.

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Speaker 3: I just don't know. We don't have that, and I

427
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Speaker 3: want it. I want it. But I was looking.

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Speaker 2: When I started looking, I was like, why is you know,

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Speaker 2: I'm in Google, Like, why is winter more so awful?

430
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Speaker 2: So awful in Atlanta? It's so great here, Okay. So,

431
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Speaker 2: and I found this different kind of winter that I

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Speaker 2: hadn't heard of. So, you know, we saw a volcanic

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Speaker 2: eruption a couple of weeks ago that was really devastating

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Speaker 2: in the Southern Pacific Ocean in Tonga. And when I

435
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Speaker 2: was looking at that and looking up different stuff about winter.

436
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Speaker 2: I came across the volcanic winter. Anybody ever heard of

437
00:22:38,680 --> 00:22:42,080
Speaker 2: that before? I want to see somebody says, yes, well

438
00:22:42,240 --> 00:22:44,760
Speaker 2: I hadn't heard of it me either, And we're gonna

439
00:22:44,760 --> 00:22:45,480
Speaker 2: talk about it real quick.

440
00:22:45,520 --> 00:22:45,800
Speaker 4: Okay.

441
00:22:45,880 --> 00:22:49,880
Speaker 2: So a volcanic winter is basically you have a volcano

442
00:22:49,960 --> 00:22:52,320
Speaker 2: that erupts and it is pushing up so much ash,

443
00:22:52,320 --> 00:22:55,200
Speaker 2: and you have a sulfur as well that you basically

444
00:22:55,240 --> 00:22:59,760
Speaker 2: create this cloud of debris and particles in the stratosphere.

445
00:23:00,280 --> 00:23:03,200
Speaker 2: Now we won't get into like what all those little

446
00:23:03,240 --> 00:23:04,080
Speaker 2: things are and what's happening.

447
00:23:04,119 --> 00:23:04,919
Speaker 3: I won't walk you through that.

448
00:23:04,960 --> 00:23:08,720
Speaker 2: But what you basically create, or what the environment or

449
00:23:08,720 --> 00:23:11,560
Speaker 2: the atmosphere creates is now want to switch to summer.

450
00:23:11,600 --> 00:23:13,920
Speaker 2: You know those little reflective things you can put in

451
00:23:13,920 --> 00:23:15,560
Speaker 2: your windshield so the car doesn't get too hot.

452
00:23:15,920 --> 00:23:17,119
Speaker 3: It basically creates that in the.

453
00:23:17,119 --> 00:23:21,680
Speaker 2: Atmosphere, right, And so any sun that's coming down gets

454
00:23:21,680 --> 00:23:23,879
Speaker 2: reflected back any heat, and so what you get is

455
00:23:24,720 --> 00:23:28,320
Speaker 2: effective winter. So global decrease in temperature two to three

456
00:23:28,359 --> 00:23:33,000
Speaker 2: degrees globally just from a powerful volcanic eruption.

457
00:23:33,480 --> 00:23:34,560
Speaker 3: And I was like, is.

458
00:23:34,560 --> 00:23:36,760
Speaker 2: This for real when I first saw it, because I

459
00:23:36,760 --> 00:23:38,840
Speaker 2: hadn't heard of it before. I felt like everybody did

460
00:23:38,840 --> 00:23:41,080
Speaker 2: a science project with a volcano, they didn't talk about

461
00:23:41,080 --> 00:23:45,240
Speaker 2: it then, No, And so I was like, wow, I'm

462
00:23:45,280 --> 00:23:48,040
Speaker 2: not sure. I don't know about this. And I looked

463
00:23:48,040 --> 00:23:51,120
Speaker 2: a little bit more beyond Wikipedia, and I said, oh,

464
00:23:51,160 --> 00:23:52,600
Speaker 2: it looks like the last time this happened was in

465
00:23:52,640 --> 00:23:54,720
Speaker 2: like nineteen twelve. But they can track this back to

466
00:23:54,880 --> 00:23:58,520
Speaker 2: so many years ago. And I was like, well, what's

467
00:23:58,520 --> 00:24:02,119
Speaker 2: the real like, what's the real evidence of this? And

468
00:24:02,359 --> 00:24:05,760
Speaker 2: there is some they're taking full ice cores, So y'all

469
00:24:05,640 --> 00:24:08,520
Speaker 2: all probably familiar with ice cores. We don't have that, okay,

470
00:24:08,640 --> 00:24:10,880
Speaker 2: But they're taking these ice cores and going so deep

471
00:24:10,880 --> 00:24:13,399
Speaker 2: and pulling the ice out, and they can see volcanic

472
00:24:13,680 --> 00:24:16,119
Speaker 2: ash covering. So if you look at that thick gray

473
00:24:16,240 --> 00:24:19,560
Speaker 2: ring here in this cylinder, evidence of a volcanic winter.

474
00:24:19,800 --> 00:24:23,639
Speaker 1: So that's kind of like tree rings or the or

475
00:24:23,680 --> 00:24:26,400
Speaker 1: in rock faces. How they can look back and look

476
00:24:26,400 --> 00:24:27,760
Speaker 1: at like striations and things like that.

477
00:24:28,160 --> 00:24:31,000
Speaker 2: Now I love this kind of science. It feels really exciting.

478
00:24:31,080 --> 00:24:33,280
Speaker 2: It feels I can see it with the naked eye,

479
00:24:33,280 --> 00:24:35,640
Speaker 2: compared to like molecular biology, which is what I've done.

480
00:24:36,320 --> 00:24:38,439
Speaker 2: But It also feels a little unsafe, like do we

481
00:24:38,520 --> 00:24:41,040
Speaker 2: know this isn't a load bearing ice core? You know,

482
00:24:41,119 --> 00:24:43,320
Speaker 2: Like I just feel like we shouldn't take too many

483
00:24:43,359 --> 00:24:45,400
Speaker 2: of these, you know, punch out right from the earth.

484
00:24:45,440 --> 00:24:47,680
Speaker 2: I feel like somebody should be regular right the.

485
00:24:47,720 --> 00:24:48,720
Speaker 4: Whole punch in the earth.

486
00:24:48,960 --> 00:24:51,119
Speaker 1: Something's gonna happen, Something bad is gonna happen.

487
00:24:51,440 --> 00:24:53,239
Speaker 2: I've seen a lot of movies, and that's usually how

488
00:24:53,240 --> 00:24:56,080
Speaker 2: it starts. Now, this is what I want to ask

489
00:24:56,119 --> 00:24:56,520
Speaker 2: you all about.

490
00:24:56,640 --> 00:24:58,840
Speaker 4: Yes, we've got questions, we've.

491
00:24:58,640 --> 00:25:00,800
Speaker 2: Got Now I'm interested. I'm gonna tell you I'm in

492
00:25:00,920 --> 00:25:01,679
Speaker 2: thermal culture.

493
00:25:01,920 --> 00:25:02,280
Speaker 4: M hm.

494
00:25:02,840 --> 00:25:06,600
Speaker 2: So y'all are dipping in different hot water bests while

495
00:25:06,600 --> 00:25:10,480
Speaker 2: it's cold outside. They go from hot water than cold water,

496
00:25:11,520 --> 00:25:12,800
Speaker 2: then back to hot, then.

497
00:25:12,800 --> 00:25:15,119
Speaker 4: Back to cold. I don't know how many cycles y'all do,

498
00:25:15,520 --> 00:25:17,560
Speaker 4: but it's different.

499
00:25:17,640 --> 00:25:18,280
Speaker 3: It's different.

500
00:25:18,640 --> 00:25:20,760
Speaker 2: Now we don't we haven't figured out the science behind

501
00:25:20,760 --> 00:25:21,240
Speaker 2: this one.

502
00:25:21,520 --> 00:25:23,120
Speaker 3: But we're trying to.

503
00:25:23,040 --> 00:25:25,320
Speaker 2: Find a way to make it an episode, okay, and

504
00:25:25,359 --> 00:25:27,240
Speaker 2: we're gonna have to shout out all of y'all yeah,

505
00:25:27,240 --> 00:25:28,919
Speaker 2: because you know, this is your own thing. So when

506
00:25:28,960 --> 00:25:30,760
Speaker 2: we do it, we're gonna you know, on social media,

507
00:25:30,800 --> 00:25:32,360
Speaker 2: we're gonna ask for a call. We're gonna say, hey,

508
00:25:32,480 --> 00:25:34,640
Speaker 2: let us know what you think. If you've ever done this,

509
00:25:34,720 --> 00:25:37,080
Speaker 2: I want y'all to call in and tell us. Okay,

510
00:25:37,359 --> 00:25:38,119
Speaker 2: we want to hear it.

511
00:25:39,920 --> 00:25:42,720
Speaker 1: And so another thing that we found that was very

512
00:25:42,720 --> 00:25:45,120
Speaker 1: interesting when we were, you know, doing our googles, why

513
00:25:45,320 --> 00:25:48,679
Speaker 1: is winter is so awful but seems so manageable to

514
00:25:48,800 --> 00:25:50,040
Speaker 1: people in the Midwest.

515
00:25:51,280 --> 00:25:58,920
Speaker 4: Apparently you are smarter. Yes, I think it's fair. Big

516
00:25:59,000 --> 00:26:01,400
Speaker 4: up yourselves, Big up to y'all.

517
00:26:02,400 --> 00:26:05,919
Speaker 1: There have been studies that have shown that folks that

518
00:26:05,960 --> 00:26:09,760
Speaker 1: are in cooler climates are actually smarter. They make better

519
00:26:09,920 --> 00:26:12,919
Speaker 1: decisions than people in warmer climates. And the studies that

520
00:26:12,920 --> 00:26:15,160
Speaker 1: they were doing is that they would have so apparently

521
00:26:15,560 --> 00:26:17,919
Speaker 1: a comfortable temperature is seventy two degrees. I know that

522
00:26:17,960 --> 00:26:23,000
Speaker 1: probably sounds balmy to y'all, but so they put folks

523
00:26:23,040 --> 00:26:25,920
Speaker 1: that were in a room colder than seventy two degrees

524
00:26:26,000 --> 00:26:29,000
Speaker 1: five five degrees and then warmer, and they gave them

525
00:26:29,280 --> 00:26:33,359
Speaker 1: a series of tasks. In almost one hundred percent of

526
00:26:33,400 --> 00:26:36,760
Speaker 1: the time, the people who were in the cooler rooms

527
00:26:36,880 --> 00:26:40,399
Speaker 1: performed way better. And it's not even that they were

528
00:26:40,440 --> 00:26:44,960
Speaker 1: able to perform better. They were able to choose routes.

529
00:26:44,600 --> 00:26:47,720
Speaker 4: To get to an answer that made more sense.

530
00:26:47,760 --> 00:26:51,520
Speaker 1: They were more logical and they didn't stray away from

531
00:26:52,040 --> 00:26:53,080
Speaker 1: complex thinking.

532
00:26:53,280 --> 00:26:56,320
Speaker 4: The people in warmer in the warmer rooms.

533
00:26:56,080 --> 00:26:58,159
Speaker 2: They would always just kind of take the easiest route

534
00:26:58,160 --> 00:26:59,920
Speaker 2: to be able to finish whatever they were doing.

535
00:27:00,760 --> 00:27:03,400
Speaker 4: So that was really really interesting to me, and I said, Wow,

536
00:27:03,440 --> 00:27:07,440
Speaker 4: I guess, I guess maybe the people.

537
00:27:07,119 --> 00:27:09,840
Speaker 2: In Minnesota know what they're doing and they they've got it.

538
00:27:10,000 --> 00:27:12,040
Speaker 2: They've got it all figured out. They know exactly what

539
00:27:12,240 --> 00:27:13,280
Speaker 2: I mean. I saw the skywalking.

540
00:27:13,359 --> 00:27:17,280
Speaker 4: That seems smart, genius, smart genius. I was like, you

541
00:27:17,280 --> 00:27:19,320
Speaker 4: don't even have to go outside. This is perfect.

542
00:27:20,280 --> 00:27:23,960
Speaker 2: But then I was like, are they smarter because they

543
00:27:23,960 --> 00:27:28,480
Speaker 2: also do things like this, and y'all seem to do

544
00:27:28,560 --> 00:27:29,960
Speaker 2: it a lot and like to do it, and I

545
00:27:30,000 --> 00:27:34,080
Speaker 2: don't really understand. It feels dangerous, it feels very dangerous.

546
00:27:34,160 --> 00:27:37,280
Speaker 2: But luckily for y'all, I've taken a lot of physics,

547
00:27:37,320 --> 00:27:40,920
Speaker 2: so I was able to I was able to understand

548
00:27:40,960 --> 00:27:42,840
Speaker 2: it a little bit more so for someone like the

549
00:27:42,880 --> 00:27:43,440
Speaker 2: ski jump.

550
00:27:44,640 --> 00:27:46,399
Speaker 1: I'm sure you've all seen it. The Winter Olympics is

551
00:27:46,480 --> 00:27:49,720
Speaker 1: upon us. We are very excited about it. With the

552
00:27:49,720 --> 00:27:53,000
Speaker 1: ski jump. They go down that long ramp and they

553
00:27:53,080 --> 00:27:56,760
Speaker 1: launch themselves into the sky and they all make the

554
00:27:56,800 --> 00:28:00,639
Speaker 1: same shape with their body, and that's intentional, of course,

555
00:28:01,400 --> 00:28:02,920
Speaker 1: and it's because of physics.

556
00:28:03,119 --> 00:28:04,480
Speaker 4: They're making this shape with.

557
00:28:04,440 --> 00:28:09,439
Speaker 1: Their body that is one aerodynamic but also gives them

558
00:28:09,480 --> 00:28:13,479
Speaker 1: the ability to fly, so it gives them lift. So

559
00:28:14,520 --> 00:28:18,320
Speaker 1: it's the same thought process, the same physics that's used

560
00:28:18,320 --> 00:28:20,959
Speaker 1: for airplane wings. You can see that the shape is

561
00:28:21,200 --> 00:28:24,679
Speaker 1: roughly the same. Right, the air that's passing over a

562
00:28:24,720 --> 00:28:28,840
Speaker 1: ski jumper is going a lot faster, and what that

563
00:28:28,920 --> 00:28:31,920
Speaker 1: does is it creates a difference in pressure, so there's

564
00:28:32,000 --> 00:28:35,440
Speaker 1: higher pressure beneath them, and that pushes them up into

565
00:28:35,440 --> 00:28:38,800
Speaker 1: the sky. Now they don't have an engine on their backs,

566
00:28:39,120 --> 00:28:40,240
Speaker 1: so they got.

567
00:28:40,040 --> 00:28:40,920
Speaker 4: To come down to the ground.

568
00:28:40,960 --> 00:28:43,440
Speaker 1: And that's well, that's not the only part that scares me,

569
00:28:43,480 --> 00:28:45,920
Speaker 1: but they got to come down to the ground. So

570
00:28:46,720 --> 00:28:50,000
Speaker 1: once they reach their peak, they start to make their

571
00:28:50,040 --> 00:28:52,680
Speaker 1: way down, they change the shape of their skis and

572
00:28:52,720 --> 00:28:54,520
Speaker 1: then when they hit the ground, they always make sure

573
00:28:54,520 --> 00:28:57,840
Speaker 1: they have bent knees and they are able to distribute

574
00:28:57,960 --> 00:29:00,760
Speaker 1: the energy that they have accumulated from going down the

575
00:29:00,840 --> 00:29:04,240
Speaker 1: ramp so fast, flying through the sky and distribute that

576
00:29:04,400 --> 00:29:09,560
Speaker 1: energy into the ground and not into their bones and crash.

577
00:29:09,600 --> 00:29:14,280
Speaker 1: So that is something that I think you gotta be

578
00:29:14,360 --> 00:29:16,400
Speaker 1: pretty smart to be able to do that.

579
00:29:16,560 --> 00:29:18,440
Speaker 4: So it lines up with the science.

580
00:29:18,520 --> 00:29:21,000
Speaker 2: I mean, I think we gotta figure it out, Like

581
00:29:21,120 --> 00:29:23,760
Speaker 2: y'all know something that we don't know yet on the

582
00:29:23,760 --> 00:29:26,760
Speaker 2: East coast. Yeah, because meanwhile on the East Coast, a

583
00:29:26,800 --> 00:29:33,520
Speaker 2: few weeks ago, we got a whopping four inches of snow. Okay,

584
00:29:33,760 --> 00:29:36,400
Speaker 2: imagine it.

585
00:29:36,320 --> 00:29:39,800
Speaker 1: Was a dark time for us.

586
00:29:40,600 --> 00:29:42,920
Speaker 4: We were struggling. These people, these are real pictures.

587
00:29:42,920 --> 00:29:46,280
Speaker 1: They were stranded overnight on the highway from four inches

588
00:29:46,280 --> 00:29:46,680
Speaker 1: of snow.

589
00:29:47,040 --> 00:29:48,200
Speaker 4: Because we don't know what we're doing.

590
00:29:48,760 --> 00:29:53,600
Speaker 2: And there's a warm temperature decisions Okay, they need to

591
00:29:53,640 --> 00:29:55,400
Speaker 2: lower the temperature when they decide how we're going to

592
00:29:55,480 --> 00:29:56,000
Speaker 2: prepare for.

593
00:29:55,960 --> 00:29:59,080
Speaker 1: The snow exactly. And I mean we've had This isn't

594
00:29:59,080 --> 00:29:59,960
Speaker 1: the first time this has happen.

595
00:30:00,560 --> 00:30:03,360
Speaker 2: Now, this a couple of Ooh, I don't know how

596
00:30:03,360 --> 00:30:04,600
Speaker 2: long ago it was, but t T and I were

597
00:30:04,640 --> 00:30:05,240
Speaker 2: in grad school.

598
00:30:05,520 --> 00:30:07,960
Speaker 3: This is in Durham, North Carolina. Yes, this is a

599
00:30:08,000 --> 00:30:10,640
Speaker 3: story were gonna tell often, but y'all are our.

600
00:30:10,520 --> 00:30:13,840
Speaker 2: Friends now, so yeah, we're gonna tell it. We both

601
00:30:13,840 --> 00:30:15,240
Speaker 2: are in the lab and I'm like, oh girl, it's

602
00:30:15,280 --> 00:30:16,040
Speaker 2: gonna snow today.

603
00:30:16,640 --> 00:30:17,480
Speaker 3: They always say it's.

604
00:30:17,360 --> 00:30:21,160
Speaker 2: Gonna snows neverth Carolina. And I'm like, hmmm, we're gonna

605
00:30:21,200 --> 00:30:23,440
Speaker 2: go out, you know. So we're gonna do our experiments,

606
00:30:23,480 --> 00:30:25,479
Speaker 2: set everything up. We can go out after after our

607
00:30:25,520 --> 00:30:28,280
Speaker 2: experiments are done. And then they're like, no, no, the

608
00:30:28,360 --> 00:30:31,800
Speaker 2: ice is coming. It's ice and snow. Everything comes to

609
00:30:31,840 --> 00:30:34,560
Speaker 2: a screeching halt. So we're at Duke at the time.

610
00:30:34,680 --> 00:30:37,440
Speaker 2: We're in the labs, but Duke is a major employer

611
00:30:37,760 --> 00:30:41,080
Speaker 2: in Durham, North Carolina. But also when Duke makes a decision,

612
00:30:41,680 --> 00:30:45,440
Speaker 2: so does everybody else. So the whole city of Durham

613
00:30:45,560 --> 00:30:49,600
Speaker 2: just says two o'clock. Everything's canceled, everything's closed.

614
00:30:51,840 --> 00:30:52,160
Speaker 4: Crying.

615
00:30:52,680 --> 00:30:53,560
Speaker 3: Yeah, I mean.

616
00:30:54,880 --> 00:30:57,280
Speaker 2: So we both park in the same parking garage. I

617
00:30:57,400 --> 00:31:00,160
Speaker 2: was on like the top floor. It took us so

618
00:31:00,240 --> 00:31:04,000
Speaker 2: long to get down out of the garage, and I said,

619
00:31:04,040 --> 00:31:05,760
Speaker 2: tit grow, What do you think is happening? So we're

620
00:31:05,800 --> 00:31:08,560
Speaker 2: facetiming each other okay in the car because we've been

621
00:31:08,560 --> 00:31:10,520
Speaker 2: in there so long, and I said this, you know,

622
00:31:10,600 --> 00:31:11,680
Speaker 2: it's been thirty minutes.

623
00:31:11,800 --> 00:31:13,160
Speaker 3: That's a long time to be to.

624
00:31:13,120 --> 00:31:15,920
Speaker 2: Be in the garage trying to get out, and the

625
00:31:15,960 --> 00:31:18,960
Speaker 2: things I'm thinking about are like, so much exhaust?

626
00:31:19,320 --> 00:31:22,680
Speaker 3: Should we all be here like this? You know, it

627
00:31:22,720 --> 00:31:23,479
Speaker 3: doesn't seem right now.

628
00:31:23,520 --> 00:31:25,040
Speaker 2: Normally the cars come and go, so you have time

629
00:31:25,080 --> 00:31:25,880
Speaker 2: for this stuff to clear.

630
00:31:27,080 --> 00:31:29,040
Speaker 3: Do I have enough gas? I waited the last minute

631
00:31:29,080 --> 00:31:30,959
Speaker 3: for everything. Do I have enough gas? If we continue

632
00:31:31,000 --> 00:31:32,120
Speaker 3: to wait here and idle, I.

633
00:31:32,040 --> 00:31:33,280
Speaker 4: Thought we were gonna have to sleep there.

634
00:31:34,400 --> 00:31:37,200
Speaker 2: I mean, just to give you a taste of the

635
00:31:37,280 --> 00:31:40,560
Speaker 2: level of panic. We're in line, waiting to get still

636
00:31:40,600 --> 00:31:42,320
Speaker 2: waiting to get out of the garage, and somebody comes

637
00:31:42,360 --> 00:31:45,080
Speaker 2: and knocks on my window, and it is a woman

638
00:31:45,120 --> 00:31:48,480
Speaker 2: from my lab. She said, can you take me home

639
00:31:48,520 --> 00:31:50,479
Speaker 2: to Chapel Hill, which is about thirty minutes from here.

640
00:31:50,480 --> 00:31:52,400
Speaker 2: I'm like, girl, I've been here thirty minutes. I haven't moved,

641
00:31:52,400 --> 00:31:53,880
Speaker 2: but one level, I won't be able.

642
00:31:53,760 --> 00:31:54,200
Speaker 3: To take you home.

643
00:31:54,240 --> 00:31:55,640
Speaker 2: I said, what about your car? She said, I'll just

644
00:31:55,720 --> 00:31:57,360
Speaker 2: leave it. Just take me as far as you can,

645
00:31:57,440 --> 00:31:58,800
Speaker 2: as far as you can go, and then I'll get

646
00:31:58,800 --> 00:32:02,120
Speaker 2: out of our walk. I mean, people were making those

647
00:32:02,160 --> 00:32:04,800
Speaker 2: types of decisions at four inches of snow, and I

648
00:32:04,840 --> 00:32:06,120
Speaker 2: thought that she was being wild.

649
00:32:06,120 --> 00:32:07,720
Speaker 3: But when we made it out to the street, When.

650
00:32:07,600 --> 00:32:10,360
Speaker 2: We made it out to the street, it was as

651
00:32:10,440 --> 00:32:13,640
Speaker 2: if the apocalypse had happened. People had abandoned that abandoned

652
00:32:13,680 --> 00:32:14,080
Speaker 2: their cars.

653
00:32:14,080 --> 00:32:15,040
Speaker 4: That's the reason why we were stuck.

654
00:32:15,040 --> 00:32:17,840
Speaker 1: People just got out their cars and left their cars there.

655
00:32:18,920 --> 00:32:20,080
Speaker 3: I know that would not fly here.

656
00:32:20,080 --> 00:32:21,080
Speaker 4: I know y'all wouldn't have.

657
00:32:21,160 --> 00:32:24,720
Speaker 2: Had first of all, the streets would be clean, yes,

658
00:32:24,800 --> 00:32:29,320
Speaker 2: And people were just leaving their cars parked at the light,

659
00:32:29,960 --> 00:32:32,440
Speaker 2: and so we were waiting.

660
00:32:32,160 --> 00:32:33,760
Speaker 4: Behind cars and didn't have people in them.

661
00:32:35,120 --> 00:32:39,440
Speaker 1: We both lived less than five miles away from campus.

662
00:32:39,800 --> 00:32:42,920
Speaker 1: It took us four hours to get home.

663
00:32:44,280 --> 00:32:45,520
Speaker 3: I thought they learned something from that.

664
00:32:45,800 --> 00:32:46,280
Speaker 4: They didn't.

665
00:32:46,320 --> 00:32:47,640
Speaker 3: They didn't. They didn't, they didn't.

666
00:32:47,680 --> 00:32:49,160
Speaker 4: We haven't learned a thing on the East Coast.

667
00:32:50,560 --> 00:32:53,280
Speaker 1: But one of the things that gets really impacted on

668
00:32:53,320 --> 00:32:55,479
Speaker 1: the East Coast when things like this happened.

669
00:32:55,520 --> 00:32:56,920
Speaker 4: I know you saw in those pictures that there were

670
00:32:56,920 --> 00:32:57,480
Speaker 4: some trucks.

671
00:32:58,280 --> 00:33:02,760
Speaker 1: The trucking industry gets severely impacted every time something like

672
00:33:02,800 --> 00:33:06,280
Speaker 1: this happens. Like we make jokes about it, but there

673
00:33:06,280 --> 00:33:09,479
Speaker 1: are truckers that are really struggling because they are trying

674
00:33:09,520 --> 00:33:11,959
Speaker 1: to get us the things that we need in our

675
00:33:12,000 --> 00:33:14,240
Speaker 1: grocery stores and the things that we are adding to

676
00:33:14,280 --> 00:33:19,240
Speaker 1: cart on Amazon. So one of the episodes that we

677
00:33:19,280 --> 00:33:24,920
Speaker 1: did recently was about the trucker shortage, and calling it

678
00:33:24,920 --> 00:33:29,160
Speaker 1: a trucker shortage kind of is misleading because there are

679
00:33:29,240 --> 00:33:33,960
Speaker 1: over ten million people with a CDL, so a commercial what.

680
00:33:33,920 --> 00:33:35,560
Speaker 3: Is cdo commercial drivers.

681
00:33:35,320 --> 00:33:36,760
Speaker 4: Like commercial drivers?

682
00:33:36,800 --> 00:33:41,960
Speaker 1: But only a third of them are trucking And there's

683
00:33:42,000 --> 00:33:45,080
Speaker 1: a reason. There's the reason for that is because the

684
00:33:45,120 --> 00:33:48,720
Speaker 1: trucking industry is modeling themselves after the fat food industry,

685
00:33:49,640 --> 00:33:53,200
Speaker 1: and what they bank on, what they hope for is

686
00:33:53,200 --> 00:33:56,560
Speaker 1: that they can get people that are really excited, dedicated

687
00:33:57,200 --> 00:34:01,280
Speaker 1: to become truckers and then they burn them out and

688
00:34:01,320 --> 00:34:03,560
Speaker 1: then another person will come in right behind them.

689
00:34:03,840 --> 00:34:10,040
Speaker 2: That sounds a little bit like nursing science academics.

690
00:34:10,080 --> 00:34:13,960
Speaker 1: I'm like, yes, right, it sounds like a lot of

691
00:34:14,000 --> 00:34:17,000
Speaker 1: industries and you can that. I'm telling you go back

692
00:34:17,040 --> 00:34:19,920
Speaker 1: and listen to this episode because you'll find that there

693
00:34:19,920 --> 00:34:23,160
Speaker 1: are a lot of tactics that overlay in a lot

694
00:34:23,200 --> 00:34:25,399
Speaker 1: of the industries that we work in. And so you'll

695
00:34:25,440 --> 00:34:28,080
Speaker 1: start to be like, wait a minute, are you trying

696
00:34:28,080 --> 00:34:29,319
Speaker 1: to what's going on here?

697
00:34:30,400 --> 00:34:32,839
Speaker 4: And like all of these ways.

698
00:34:32,520 --> 00:34:37,000
Speaker 1: That we're being monitored and managed and being asked to

699
00:34:37,040 --> 00:34:42,320
Speaker 1: put out really really high productivity rates that seem impossible.

700
00:34:42,719 --> 00:34:45,680
Speaker 1: It all goes back to the the invention of the

701
00:34:45,719 --> 00:34:48,640
Speaker 1: factory in the assembly line. What they did is that

702
00:34:48,680 --> 00:34:52,520
Speaker 1: they had this thing called scientific management where they would

703
00:34:52,520 --> 00:34:56,440
Speaker 1: find the biggest, toughest guy in the in the whole factory,

704
00:34:56,640 --> 00:34:59,799
Speaker 1: and they would say, over the next hour, I want

705
00:34:59,840 --> 00:35:03,439
Speaker 1: you to make as many of these things as you can.

706
00:35:04,400 --> 00:35:06,280
Speaker 1: And if he could make a hundred, they would say,

707
00:35:06,680 --> 00:35:10,120
Speaker 1: that's the that's the line. Everybody needs to be making

708
00:35:10,200 --> 00:35:13,319
Speaker 1: a hundred of these an hour. And so then they

709
00:35:13,360 --> 00:35:16,680
Speaker 1: would dictate what your productivity or how productive you were

710
00:35:17,120 --> 00:35:19,080
Speaker 1: based on that benchmark.

711
00:35:20,200 --> 00:35:22,160
Speaker 2: And that's the same tactic that they use in a

712
00:35:22,280 --> 00:35:25,360
Speaker 2: lot of different industries, including the trucking industry. And so

713
00:35:25,400 --> 00:35:29,759
Speaker 2: when we have things like that, should we call the snowstorm.

714
00:35:29,840 --> 00:35:30,319
Speaker 4: I don't know.

715
00:35:32,920 --> 00:35:37,759
Speaker 2: Yes, we didn't have any This is my actual grocery store.

716
00:35:37,800 --> 00:35:38,680
Speaker 4: We didn't have any food.

717
00:35:40,040 --> 00:35:41,479
Speaker 3: This is a couple weeks ago.

718
00:35:41,600 --> 00:35:42,959
Speaker 4: This is a couple of weeks ago. I've been eaten

719
00:35:43,239 --> 00:35:47,200
Speaker 4: since then. This is my first time eating. There was

720
00:35:47,280 --> 00:35:48,439
Speaker 4: no there was no food.

721
00:35:48,480 --> 00:35:50,399
Speaker 1: The only food that was on the shelves were things

722
00:35:50,400 --> 00:35:53,680
Speaker 1: that were prepackaged, so you couldn't find any fresh vegetables.

723
00:35:53,760 --> 00:35:56,399
Speaker 1: There's no meat, there was no milk, there was nothing.

724
00:35:56,400 --> 00:35:57,160
Speaker 3: There was plant milk.

725
00:35:57,360 --> 00:35:59,800
Speaker 4: There was plant based milk, which is just fine, but

726
00:36:00,480 --> 00:36:00,879
Speaker 4: there was.

727
00:36:01,120 --> 00:36:03,839
Speaker 1: A lot of people were everybody came to the grocery store.

728
00:36:03,840 --> 00:36:06,200
Speaker 1: We all just stared at the empty shells, like, what

729
00:36:06,960 --> 00:36:09,360
Speaker 1: is actually going on? And it's because of all this

730
00:36:09,520 --> 00:36:13,200
Speaker 1: backup and there's not enough truckers that are being valued

731
00:36:13,520 --> 00:36:16,640
Speaker 1: and put into a situation where they can flourish and

732
00:36:17,080 --> 00:36:19,640
Speaker 1: do their jobs and do their jobs for a long time.

733
00:36:20,239 --> 00:36:21,680
Speaker 1: And so that was one of the things that we

734
00:36:21,760 --> 00:36:26,080
Speaker 1: found when we were researching for this episode. Lap thirty

735
00:36:26,160 --> 00:36:30,520
Speaker 1: nine ad Decartes. Seventy percent of all freight is transported

736
00:36:30,520 --> 00:36:31,359
Speaker 1: by trucks.

737
00:36:31,880 --> 00:36:33,640
Speaker 4: The average truck driver on.

738
00:36:33,680 --> 00:36:36,360
Speaker 1: The road is driving twenty one days out of the

739
00:36:36,400 --> 00:36:38,520
Speaker 1: month and doing fourteen hour days.

740
00:36:39,000 --> 00:36:41,200
Speaker 3: That's a lot of time.

741
00:36:41,960 --> 00:36:43,560
Speaker 4: I don't do anything that much.

742
00:36:43,920 --> 00:36:45,040
Speaker 3: Sometimes you sleep that long.

743
00:36:45,120 --> 00:36:48,400
Speaker 4: I can sleep for a very long time, yes, but

744
00:36:48,480 --> 00:36:50,080
Speaker 4: even that I can't do that for that long.

745
00:36:50,120 --> 00:36:51,600
Speaker 2: I feel like we really got to give the truckers

746
00:36:51,600 --> 00:36:53,759
Speaker 2: their props, right, like and you know, when I was

747
00:36:53,760 --> 00:36:55,279
Speaker 2: a kid, I used to go like this when I

748
00:36:55,320 --> 00:36:57,239
Speaker 2: saw it, Yes, I feel like we need to like

749
00:36:57,280 --> 00:36:59,040
Speaker 2: do this, but then do a heart. Yeah, like we

750
00:36:59,040 --> 00:37:00,680
Speaker 2: should do something when we see them.

751
00:37:00,760 --> 00:37:05,200
Speaker 1: Honestly, they're working very very hard and they really don't

752
00:37:05,440 --> 00:37:08,440
Speaker 1: get the necessary props that they should be getting. And

753
00:37:08,640 --> 00:37:10,279
Speaker 1: the other thing is that they don't also don't get

754
00:37:10,320 --> 00:37:13,800
Speaker 1: any money when they stop yea like when they stop driving,

755
00:37:13,800 --> 00:37:17,520
Speaker 1: when they're when they're dropping their goods off, the clock stops,

756
00:37:17,520 --> 00:37:19,880
Speaker 1: they stop making money. And so there's a lot of

757
00:37:19,880 --> 00:37:22,799
Speaker 1: initiatives out there that's trying to get them better pay

758
00:37:22,880 --> 00:37:26,440
Speaker 1: for their work, but it's just really, really tough. So

759
00:37:27,160 --> 00:37:29,279
Speaker 1: that's what happens on the East Coast. I mean, I

760
00:37:29,280 --> 00:37:32,560
Speaker 1: think it again to go back to the if it's colder,

761
00:37:32,600 --> 00:37:33,120
Speaker 1: you're smarter.

762
00:37:33,520 --> 00:37:34,560
Speaker 3: Yeah, we haven't quite figured.

763
00:37:34,760 --> 00:37:37,800
Speaker 2: We haven't we're not cold enough, but you know, yeah,

764
00:37:37,920 --> 00:37:42,120
Speaker 2: you really have to kind of say, like we should

765
00:37:42,200 --> 00:37:43,880
Speaker 2: learn from each other. So I'm gonna try to take

766
00:37:43,880 --> 00:37:45,439
Speaker 2: some tips that I see you around here. When trying

767
00:37:45,440 --> 00:37:46,800
Speaker 2: to take some of that back with me to Georgia,

768
00:37:47,080 --> 00:37:49,160
Speaker 2: you'll maybe take something back with you to Maryland. And

769
00:37:49,200 --> 00:37:52,439
Speaker 2: I know y'all all probably I'll probably take food. She will,

770
00:37:52,520 --> 00:37:55,000
Speaker 2: she will, And y'all have been rocking with us throughout this.

771
00:37:55,200 --> 00:37:57,399
Speaker 2: And you may say I thought they were talking about winter.

772
00:37:57,760 --> 00:37:59,160
Speaker 2: How are we talking about trucks?

773
00:38:00,560 --> 00:38:00,799
Speaker 3: You know?

774
00:38:00,960 --> 00:38:02,880
Speaker 2: I think we just are really excited to show you

775
00:38:02,960 --> 00:38:05,080
Speaker 2: were just giving you just a tiny sliver, like if

776
00:38:05,120 --> 00:38:07,680
Speaker 2: we had a pie, not even a full slice of

777
00:38:08,400 --> 00:38:12,520
Speaker 2: what is like between us right like this, you're in

778
00:38:12,560 --> 00:38:14,600
Speaker 2: our brains, you're with us, You're with us, and so

779
00:38:15,120 --> 00:38:17,080
Speaker 2: you know, although we were talking about winter, I feel

780
00:38:17,120 --> 00:38:17,920
Speaker 2: like just a recap.

781
00:38:17,960 --> 00:38:19,440
Speaker 3: We've talked about quite a few things.

782
00:38:19,640 --> 00:38:19,800
Speaker 4: You know.

783
00:38:19,840 --> 00:38:21,520
Speaker 2: We talked a little bit about climate, and then we

784
00:38:21,600 --> 00:38:24,040
Speaker 2: jumped into sharing all of this water, and then we said,

785
00:38:24,120 --> 00:38:26,040
Speaker 2: let's do it for the bivalves, you know, so that's

786
00:38:26,360 --> 00:38:29,880
Speaker 2: that's where our muscles and oysters and crustaceans too, even

787
00:38:29,920 --> 00:38:30,799
Speaker 2: though I don't really rock with them.

788
00:38:30,880 --> 00:38:33,960
Speaker 1: And then we talked about plastic snow, We talked about

789
00:38:34,440 --> 00:38:37,560
Speaker 1: volcanic winter. My friend told us all about those cores,

790
00:38:37,600 --> 00:38:38,680
Speaker 1: those load bearing cores.

791
00:38:39,480 --> 00:38:42,080
Speaker 2: And I think I've really settled on winter is underrated,

792
00:38:42,120 --> 00:38:43,520
Speaker 2: Like y'all are doing it right up here.

793
00:38:43,680 --> 00:38:44,600
Speaker 3: Winter's underrated.

794
00:38:44,640 --> 00:38:46,080
Speaker 4: It is, It definitely is.

795
00:38:48,120 --> 00:38:49,359
Speaker 3: You've sold us, you've sold us.

796
00:38:49,480 --> 00:38:52,320
Speaker 1: Yes, we've learned a lot about the physics of winter

797
00:38:52,520 --> 00:38:54,880
Speaker 1: and how you all are able to maneuver on the

798
00:38:54,920 --> 00:38:58,200
Speaker 1: ice and fly through the sky no problem.

799
00:38:58,239 --> 00:39:01,040
Speaker 2: But our overall thing that we got a lot to

800
00:39:01,080 --> 00:39:05,920
Speaker 2: learn from you. We have enjoyed our time with y'all,

801
00:39:06,160 --> 00:39:10,000
Speaker 2: and unfortunately I think it's over, but we have enjoyed

802
00:39:10,000 --> 00:39:11,480
Speaker 2: you and we hope that you will continue to check

803
00:39:11,560 --> 00:39:12,800
Speaker 2: us out, share us with your friends.

804
00:39:12,920 --> 00:39:15,719
Speaker 3: Yes, just as a reminder, we're only on Spotify, but.

805
00:39:15,800 --> 00:39:18,480
Speaker 2: It's free, and it's every Thursday and you can get

806
00:39:18,520 --> 00:39:19,759
Speaker 2: as many doses as you're like.

807
00:39:20,680 --> 00:39:31,680
Speaker 3: Thank you. That's it for Lab fifty seven.

808
00:39:31,880 --> 00:39:34,560
Speaker 2: Call us at two zero two five six seven seven

809
00:39:34,680 --> 00:39:37,080
Speaker 2: zero two eight and tell us what you thought. Hopefully

810
00:39:37,080 --> 00:39:39,480
Speaker 2: that convinced you that you need us near you live.

811
00:39:39,680 --> 00:39:41,839
Speaker 2: Soon you can give us an idea for a lab

812
00:39:41,880 --> 00:39:44,640
Speaker 2: we should do this semester by calling us again at

813
00:39:44,680 --> 00:39:47,640
Speaker 2: two zero two five six seven seven zero two eight.

814
00:39:47,760 --> 00:39:49,680
Speaker 1: And don't forget there's so much more for you to

815
00:39:49,760 --> 00:39:51,959
Speaker 1: dig into. On our website. You can see a cheat

816
00:39:52,000 --> 00:39:55,400
Speaker 1: sheet for all the previous labs, additional links and resources

817
00:39:55,400 --> 00:39:57,759
Speaker 1: in the show notes. Plus you can sign up for

818
00:39:57,800 --> 00:40:01,640
Speaker 1: our newsletter. Check it out at Dope Last podcast dot com.

819
00:40:01,880 --> 00:40:04,799
Speaker 1: Special thanks to the Great Northern Festival for having us.

820
00:40:04,920 --> 00:40:08,880
Speaker 2: You can find them on Instagram at the Great Northern Festival.

821
00:40:08,520 --> 00:40:10,719
Speaker 1: And you can find us on Twitter and Instagram at

822
00:40:10,719 --> 00:40:11,760
Speaker 1: Doe Blabs podcast.

823
00:40:12,000 --> 00:40:16,280
Speaker 2: TT's on Twitter and Instagram at d R Underscore t Sho.

824
00:40:16,160 --> 00:40:18,680
Speaker 4: And you can find Zakia at z said So.

825
00:40:19,320 --> 00:40:22,520
Speaker 1: Doe Labs is a Spotify original production from Mega Owned

826
00:40:22,520 --> 00:40:23,120
Speaker 1: Media Group.

827
00:40:23,200 --> 00:40:26,080
Speaker 2: Our producers are Jenny Radlett, Mask and Lydia Smith of

828
00:40:26,200 --> 00:40:30,800
Speaker 2: WaveRunner Studios. Our associate producer from Mega Ohmedia is Breanna Garrett.

829
00:40:30,920 --> 00:40:35,440
Speaker 2: Editing in sound design by Rob Smerciak, mixing by Hannes Brown.

830
00:40:35,719 --> 00:40:39,560
Speaker 2: Original music composed and produced by Taka Yasuzawa and Alex

831
00:40:39,640 --> 00:40:45,480
Speaker 2: Sugier from Spotify. Executive producer Corin Gilliard and creative producer

832
00:40:45,640 --> 00:40:52,360
Speaker 2: Miguel Contreras. Special thanks to Shirley Ramos, Jess Borison, yasmin Afifi, Kamu, Elolia,

833
00:40:52,760 --> 00:40:57,240
Speaker 2: tillkrat Key and Brian Marquis. Executive producers from Mega Owmedia Group,

834
00:40:57,480 --> 00:41:00,000
Speaker 2: r us T T Show, Dia and Zakia Watts

835
00:41:11,400 --> 00:41:11,440
Speaker 4: H