Lost in the fog of a cosmic storm Floating on
whimsical wavelengths is the norm Dancing through
the stars, chasing spectrums of light Welcome
back to Whimsical Wavelengths. This time we're
digging deep into the bedrock of discovery. This
episode is where geology meets algorithms, and
the motherlode is just a model away. We're using
computers to crunch the crust, letting machine
learning mine the patterns hidden in the data.
This episode is loaded with knowledge that's
anything but artificial, even though the intelligence
is... And the puns are terrible. So machine learning...
The topic of this episode is a bit outside of
my wheelhouse, so I have a guest, and no, not
just any guest, a co -worker from my alter ego.
Yes, so far we've had a couple of guests that
use scientific results as the basis for their
career outside of academic institutions. Let's
add one more. Before I get further into the introduction,
many months ago I had asked for someone from
my company I work for, Computational Geoscience,
to come on the podcast and talk about machine
learning and its applications in mineral exploration.
It got delayed due to getting some results and
a shuffle of personnel and more normal real life
stuff. So this conversation is months in the
making. It's also all about science. Lots of
science takes place within the corporate world.
This here is a chance to look at one aspect.
It isn't all about laboratories and classrooms.
It's also about boardrooms. There's lots of ways
to have a career in science. Now, let's set it
up. We've been mining minerals from the ground
for thousands of years. All the deposits close
to the surface and near people have been mined.
They're gone. So the deposits we have left are
either in remote places or many hundreds of meters
under the ground. That means they're harder to
find and harder to extract those shiny resources
needed for our modern world. Today we also have
a lot of data. satellite imagery topography lidar
geophysical surveys like gravity see episode
12 for an explanation on boogie gravity or magnetics
there's so much more you'll see that sites that
have a lot of data typically also have a lot
of drilling this is where they've cored into
the earth to take samples deep within i think
this is a good detour drilling especially when
the area is not near a road it's it's quite an
endeavor The drill rig is something that needs
to be moved by heavy machinery or a large truck.
Flying it into sight is not simple in many areas.
They take a lot of fuel, a crew of at least four
people to run 24 -7. Each hole, after all is
said and done, costs well over $100 ,000. And
what do you get? Cylindrical sections of rock
for the entire drill hole. Being approximately
six centimeters in diameter, the information
brought up is expensive. It's the only way to
directly observe what we infer from the surface.
Is there mineralization there? Will it be there
next? Mine. A lot is done with that core of rock.
Geochemistry, rock types, structures, like fractures,
are all written down and described. The point
is you don't want to miss with the drill. Drilling
is expensive. Whatever increases your success
rate and costs less than drilling is useful.
Enter mineral prospectivity. Geophysics, drilling,
remote sensing. With all that data, it is still
hard to find the shiny stuff. And as said, drilling
is expensive. To help combat this, we turn to
machine learning and artificial intelligence
to find patterns in the data humans may miss.
To get enlightenment on this, a data scientist
and my co -worker from the best place to work
on Earth. And no. I'm not just saying that. He
joins the show. Here with us today is a data
scientist with Computational Geosciences Incorporated.
He got his Bachelor's of Science from the University
of Leeds, a Master's from Western University,
and then transitioned to industry using his degrees
in geophysics and continues to use science to
solve problems. Please welcome Frederick Jackson
to Whimsical Wavelengths. Hi, Jeff. Thank you
for having me. I'm a big fan. Oh, that's nice.
And no, I didn't pay him to say that. For those
that are longtime listeners, the reason why I
made that joke is because the listener question
at the end of episode 11 about the Spinosaurus
controversies actually came from Frederick here.
A trick up my sleeve to make sure at least one
more person listened to the episode. But more
broadly, how does your interest in dinosaurs
and other science avenues fit into your path
to where you are today as a data scientist? Yeah.
And by the way, I follow paleontology quite closely.
And there was actually... I don't know when you'll
release this episode, but at the time of recording,
there was a paper released yesterday, I believe,
on describing a new Spinosaurus species, which
the name I forget, but it's quite a similar species
to Spinosaurus aegypticus, which you would have
discussed, but it has this really cool head crest
on the top of its head, probably for display
purposes. It's a very interesting new discovery.
So that's something your listeners might be...
be interested to hear yeah to answer your question
i think you know when i was a kid i thought dinosaurs
were big and impressive and cool and as an adult
uh i i actually think what makes them so interesting
is the kind of mystery involved in understanding
them because we you know we can't go back in
time to see what they look like we we can't clone
them and bring them back to life like uh in the
movies so it's instead there's been these kind
of centuries of detective work where you're kind
of We're only just starting to scratch the surface
of what they actually looked like and behaved
like. And I think there are a lot of parallels
between that and what we do at CGI. You know,
we're doing geological and geophysical characterization.
We're building methods that uncover the mysteries
of the earth in the same way that paleontology
uncovers the mysteries of ancient life. And I
think both kind of, they require a certain imagination
combined with evidence, like real evidence. So
what you're saying is that your job today kind
of marries some of the great things that you've
always wanted to study, but provides a practical
aspect to it moving forward, kind of like the
industry and the like. Yeah, yeah, exactly. I
think there's a common thread of, you know, I
think all scientists have the kind of thirst
for discovery in a way. Yeah, and that's like
a perfect segue to the next question, because
before we... kind of pivot towards the technical
part the process of conducting science is just
as important as the results sometimes and that's
what we're talking about so here on whimsical
wavelengths we've covered papers that have come
from universities and museums mostly as far as
like a direct paper from industry like we've
had some industry types on but they didn't bring
a paper they wrote for industry it was usually
from it was either a topic or from their phd
thesis or something like that and that's not
a problem but This is pretty sure the first one
directly from industry. Peer review is the same,
though. But there are some differences. For us,
because I'm also employed at Computational Geosciences,
what's the biggest difference between industry
science and what you would do just strictly for
academics? Yeah, I've thought about this a lot,
actually, because I obviously had a brief period
in academia and published some papers and then
took some... time off from the research side
and now i'm kind of back in the area of publishing
again i think the biggest difference is kind
of the the consequence of actually being wrong
with your results i think in academia if it doesn't
uh quite work then you you might get your null
results or whatever but and have to rethink your
your project but um you know life goes on whereas
uh if we if we tell a client to dig somewhere
and it there's nothing there then that's a very
expensive mistake for a client. So you're balancing
this scientific discovery process using the science
to find things with the kind of risk management
side of things, like generating models, which
are maybe slightly more cautious in terms of
their predictive capabilities than otherwise.
So I think it's the risk management side of things
that's a bit different. Yeah. One of the other
things I want to kind of poke at here is Peer
review is the same, so once something gets through
peer review, it doesn't really matter where it
came from too, too much. Again, it's always one
paper, so if it's industry, you should probably
see it in an academic journal from an academic
point of view eventually. You can't always just
do one, but there's also a different emphasis
on publishing in industry, isn't there? Like,
you're not just in the pursuit of knowledge,
you only publish kind of what you want to, right?
Yeah, there's always like a financial incentive
to it, I feel like. And in fact, a lot of people
don't publish, right? Like a lot of companies
will keep their trade secrets because that's
their moneymaker. And yeah, I think I disagree
with that. I think by publishing and discussing
what we do at conferences and things, I think
we're showing people that what we do is solid
and backed by science and open to public scrutiny.
I think that kind of... transparency builds a
certain trust in what we do and I think if we
can prove what we do in areas like this paper
we're doing, we're studying some areas in Australia,
I think if we can prove that it works there then
people trust it when we suggest using it in other
areas where people haven't looked before. All
right, so the paper we're actually about to dive
into with this Australia data is formulating
gold prospectivity mapping as a constrained learning
problem. It was submitted and accepted to the
Society of Exploration Geophysics for a meeting
last year for the Applied Geoscience and Energy.
Is there anything that you need to add to from
the preamble that was part of the intro, or what
problem is this trying to address? By the way,
quick update. It should be published this month.
Again, I'm not sure when you're releasing this,
but it should be published in February in the
SEG library. So it should be accessible by the
time this comes out, I would imagine. But yes,
so no, I think you did a good job. I think the
motivation really is how complicated mineral
systems are. They can occur on these tiny little
centimeter length scales, and they're formed
by incredibly complex and poorly understood.
geological causation. And traditional exploration,
I think, is very good at filling in the gaps
between your knowns. But it's the extrapolating
which is the difficult part and the thing that
AI prospectivity is attempting to approach. What
we're trying to do is to build a model which
can do that, can extrapolate from a well -known
area into an area that it doesn't know about
at all, whilst also... Managing that risk, again,
it comes back to managing risk, managing the
risk of the model hallucinating or getting confused
when we move away from the safe areas, I guess.
Okay, so let's expand some of those ideas out
right here, I guess. Prospectivity and machine
learning is essentially, it's the machine learning
side of it that you're talking about, the networking.
So the average person listening to this has heard
of large language models like ChatGPT and that
they're trained by scraping art and music and
writing from the internet. in an effort to learn
to respond more human -like and give information
and answers. So leaving the pitfalls and issues
with that process aside for now, so how does
that work in principle here? Because that's what
you're talking about, being able to extrapolate
from a well -known area to a less known area.
I'm assuming you're having to train on data very
similarly to LLMs. How is it different and how
is that kind of all taking place? Yeah, the concept
is very similar. So as you say, ChatGPT has learned
how humans communicate via its training data,
which is the internet, and it's able to extrapolate
its responses to things it hasn't heard before.
And what we're doing is we're building an algorithm
which has, in principle, if we've done our job,
it's learned how mineral systems form via the...
geological training data, and it's extrapolating
those, I guess, like formation mechanisms to
areas that it hasn't seen before in the same
way ChatGPT has. So that's the similarity. I
think the difference is the amount of data. I
think ChatGPT obviously had the whole internet
to scrape from. We don't quite have that privilege,
so ChatGPT achieves. like uh insanely creepy
level of human i don't know if you ever like
speak to it you ever do like a back and forth
conversation with it it's it's it's creepy how
yeah and full disclosure i often give it podcast
stuff and ask it to like critique my stuff and
sometimes the stuff it gives me back you're like
oh yeah Yeah, we don't quite have that level
of training data. So again, it's back to that
risk management thing. It's about how to make
it work with the limited data that we have in
the geological world. Because, you know, drilling
is expensive in geological exploration. Geochemical
sampling is often quite sparse. So it's about
all those kind of limitations. Yeah, so maybe
we'll take a quick aside there. Do you happen
to know, roughly speaking, how much a drill hole
costs? I actually have no idea, no. Oh, okay.
Well, I guess for the listener, it's usually
in the many hundreds of thousands of dollars,
depending on how many you're going to get done.
If you're just going to go do one drill hole,
it's like a million dollars because you have
to pay to get everybody on site. But then it's
usually somewhere between $200 ,000 to $300 ,000
per drill hole. I guess the depth as well depends
on the depth. It depends on the depth. It depends
on a lot of things. So if you're going to go
really deep, then it's almost a million per drill
hole. Or if you're going to do geothermal because
of the size of the drill hole, it has to be,
it'll be more expensive. But the point is being
able to de -risk those drill holes is really
important for the industry. So as you were saying,
you don't have the same amounts of data as a
lot of these LLMs because you're dealing with
an area which we only can, We only have what
information we have is what we can measure. So
the paper uses data that is either already public
or you've received some kind of special permission
to be able to publish it, right? So what is that
data that you're using to train in this paper,
which is on Australia, and build that geological
equivalent to ChatGPT? Yeah, so we're focusing
on Western Australia and Australia is very good
at releasing their data for public use. There's
a website called Geoscience Australia that merges
and makes available a lot of exploration data
and they have essentially geophysics for all
of Western Australia, which is absolutely ideal
for a test project like this. So we went on Geoscience
Australia, we assembled what we call a data cube,
which is just a fancy name for a big folder of
data. But we assembled, I think, something like
15 different data layers of public data from
the Yilgarn Craton, which is an area of Western
Australia. And that includes potential fields
data, which is your... magnetics and gravity
data, the images into the subsurface. We have
radiometrics, we have satellite imagery, and
then we have the labels, which are geochemical
samples and potentially the most important part
of the whole puzzle. And the idea is to build
up an analog to doing this on a more kind of
brownfield level, I guess, like the kind of data
that a client might potentially have at a kind
of exploration target. Okay. only because I know
the words, and I'm going to try to expand them
out here a little bit. When we deal with, I guess,
potential fields, we're talking about someone's
gone around and measured the Earth's magnetic
field or gravitational field over a wide area,
and you're taking that map and derivatives of
that map as some of your data, right? Yeah, exactly.
And for those that are not in the industry, brownfields
means like you've had enough time to collect
a lot of different data. You know that there's
something under the ground and you're moving
towards hopefully opening a mine. In a lot of
cases when we're looking for minerals, we call
it greenfields when we don't have a lot of data
and you've maybe got one or two drill holes kind
of idea. still trying to suss out whether or
not there's anything of economic worth underneath
the ground. Whereas brownfields, you're starting
to move to words of mine. That doesn't mean you'll
get there, but that's where you're moving to.
So you have a lot of data, or at least a lot
of data in the sense of geology and data sets
that I guess you can use for prospectivity. That
summed it up pretty good. Did I miss anything?
No. Yeah, perfect. And sorry, I'm used to presenting
this at conferences and things where the terminology
is more known. Yeah, but you're also talking
to me, a co -worker who is going to understand
what you say kind of instantaneously instead
of for everyone who's listening somewhere else.
I mean, don't be afraid to get nerdy, my friend.
I mean, we all love maps, or at least we do.
So I guess the next part of the conversation
comes back to the geochem that you were just
saying. The idea of like, we've already talked
about the different data layers. But the data
layers isn't really what you're training on,
right? That's just giving the information for
it to make pattern recognition with geologic
layers or geologic targets versus what LLMs do
with words, right? So you need to give it something,
and you call that the labels. Yeah, yeah. So
in machine learning, you classify your data into
features and labels. So features are... the kind
of wide area extensive geophysics in this case
or remote sensing and labels are the actual ground
truth so the the concept of machine learning
is that you're building this kind of causative
link between your labels and your features so
the the features are the observable thing which
explain the existence of the labels so in this
case the the ground truth from from the drilling
is the labels and the geophysics is the thing
which is kind of explaining the the reason for
the labels being what they are. Okay. Just out
of curiosity, there must be some like pitfalls
to this type of approach as well, right? Like
in my own head, I come up with the simple thing
is that if you're using ground geochemistry,
something could have changed for the lack of
a, obviously we wouldn't use this necessarily,
but glaciers or rivers or flash floods can move
things downstream and modify what we see. Is
that a concern here? Is it things that modify
either humans or natural events that modify the
subsurface? Is it a case of garbage in, garbage
out? Yeah, absolutely. That's the big caveat
of all AI is the garbage in, garbage out thing.
And geochemical sampling is especially prone
to that human or instrument error or transported
samples, like you were saying. And when you're
building that causative link I mentioned, then...
that can obviously throw off your entire model.
So yeah, garbage in, garbage out for sure. I
mean, again, it comes back to that risk management
thing, building these for real world applications.
And that kind of prompted our approach that we
present in this paper. Actually, there's a nice
lead into that because we don't actually care
in our approach about the exact granular noise
of a geochemical sample. We're kind of more looking
at the bigger picture, the proximity to the kind
of high grade hits rather than the misses. So
it's more resilient to that garbage because we're
kind of building this sort of probability model
based on the bigger picture rather than the kind
of individual samples. OK, and that's what is
new and novel with the paper. Is that specifically?
Yes, exactly. I think we've kind of skipped over
one part of this. So we've just kind of I've
kind of waved a magic wand and said it's machine
learning. We haven't really described what machine
learning is. Yeah, well, machine learning is
is kind of. building that kind of causative link
that I mentioned between some kind of feature
and some kind of label, and building a model
that understands that and is then able to extrapolate
it to kind of unknown areas where we don't necessarily
have that understanding. Yeah, I was going to
give a bit more into the details, really get
nerdy here. Neural networks is more where I'm
going because there's different types, right?
Not all machine learning is the same, built the
same way. yeah for sure so we're using a neural
network in this paper and a neural network is
based on the architecture of a human brain essentially
so um it's has various kind of neural nodes and
and you know connections between those nodes
and is assembling a uh kind of unquantifiable
knowledge of of those relationships uh in a in
a different way to the way that like a regression
is a machine learning method but that's learning
a very like simple linear relationship between
things this is a very complex, non -linear, like
very hard to even sort of picture in your head
really what a neural network looks like. It's
kind of beyond visualization. And that kind of
gets to how advanced these methods are. Complete
aside, you said it's a lot like the brain. Do
the two fields actually kind of intersect there
a little bit? What do you mean? Are we programming
human beings here? Are we actually taking what
we understand of a brain and actually utilizing
that information to create a neural network?
Yeah, in a way, you can kind of look at it in
that we're sort of creating an artificial geologist
in a lot of ways. A geologist that is able to
look at data and kind of use their own understanding
of the connections between the data and then
make predictions based on what it sees. Obviously,
it's a bit more... complicated than that. And
we don't want to replace our geologist friends.
We're always going to need their inputs. And
these things are being built as tools for geologists.
But it is essentially a brain that we're creating
here. Yeah, I guess it takes a geologist to understand
the output. Yeah. You're never going to replace
the geologist because they have to feed it. And
then they have to understand the output. Yeah,
absolutely. I guess that gets to the next part
of the question. Like AI and machine learning,
it's revolutionizing some aspects. In some cases,
it's more, instead of revolutionizing something,
it's like methods of marketing. I guess this
happens every time there's a new technological
disruptor that's out there. But this is peer
-reviewed. It's solid science backing with real
-world results. What's the strategy to break
through the noise around AI and deliver real
results and get people to believe in it? I mean,
it's not really to believe in it, but to understand
the results and how they can use it. There's
just so much out there right now. I mean, I wish
I knew the best strategy, Jeff. I think our strategy
is to just kind of be as honest, like scientifically
honest as possible about the capabilities of
this and also the limitations. I think a lot
of those kind of hype companies don't. discuss
the limitations and that's what makes it snake
oil in my belief. I think what we're doing by
regularly publishing and talking about what we
do is building that trust and acknowledging the
inherent faults of all AI kind of allows us to
actually work around those faults in a smart
way by integrating as much input and knowledge
from geologists and field experts to kind of
build that tool rather than the sort of vague
black box that I think has a lot of pitfalls
in our industry. Okay, so let's pivot back to
the results. So this is an evolving tool to find
mineral deposits, but what did you find by going
through this study? Was there anything new and
surprising that we can discuss here? Yeah, I
mean, there's nothing particularly revolutionary
about what we did. It's more kind of how we framed
the problem, I guess. In terms of where this
will be going in future, I think the holy grail
is to integrate that kind of 3D information.
This is a very 2D problem. And I think we want
to... There's one thing like predicting where
to drill on a map, but actually like how deep
to drill is kind of the holy grail in my opinion.
So I think the kind of evolving steps are to
look into hybrid approaches that kind of combine
the advantages of... neural networks and AI with
traditional geophysical inversions and 3D interpolations
as well. And I think we hope to be able to talk
about that in the near future. Yeah, I guess
we didn't even touch on the fact that this is
really providing a 2D map, right? Like the idea
is you're providing a heat map or something like
a heat map that you get warmer colors when you're
closer to what you're looking for. In this case
was gold, right? Yes, exactly. Yeah. So I guess
the obvious question is, why can't we do it in
3D? Like, what's the limitation? I think that
there is a computational limitation. I think
it's also just the kind of how you frame the
problem. I think this is a very unconstrained
kind of solution that we've developed. I think
a 3D solution because it's in so much more. resolution
and there's so much more information, I think
you have to introduce a lot of constraints to
that in a positive way. I think that's a good
thing to produce models which are consistent
with your actual 3D information. You just have
to parameterize that a little bit differently
and it's not something which is unachievable
and it is something that we are working on. So
you would say it's more a function of not having
3D data? Like we have some, but again, being
a geophysicist and we've covered inversions before,
inversions are non -unique. So you can use them,
but you can't use them for your labels, right?
Yeah, exactly. An inversion is a subjective interpretation
of objective data. And that's where the non -uniqueness
comes from. And there's an infinite amount of
models which satisfy a given data set. And we
do things in Inversion to try and reduce the
amount of models which do satisfy that data set.
But the more you can constrain and limit that
process, the better. So I guess more to the frame,
I'm familiar with geophysics and Inversion models.
And I guess I'm familiar with geologic models,
but generally speaking, there isn't that many
geologic models out there to be had that aren't
similar to inversion models, that they're just
interpolated between. We don't have a lot of
labels. So is part of the reason why we can't
have these 3D prospectivity more to the fact
that we just don't have 3D data? Like, how do
you bring a DEM into depth or things that you
collect on the Earth's surface into something
you can actually... The only thing I can think
of we have is the drill holes. Yeah, and drill
holes are obviously incredibly valuable that
they're expensive, but that's an objective observation
of what the lithology actually looks like. And
you often get geophysical information with that
too. So I think the real value comes from combining
that knowledge of lithology, knowledge of downhole
geophysics, and then combining that with surface
geophysics. and kind of building something that
satisfies all of those at the same time. And
it's a slightly different problem to the one
we tackled in this paper. This paper was more
about a kind of rapid 2D kind of de -risking
product. It's not supposed to be objective or
an accurate model of the subsurface. It's supposed
to be something for simple de -risking and for
exploration targeting. I think what... The next
step is this kind of 3D modeling, which builds
a slightly more targetable model in terms of
drilling. Yeah, and actually, there's a couple
of ways I've also seen this used. You're probably
aware of them as well. Like in British Columbia,
they sent out a request for proposals from companies
such as ours. We didn't take them up on this
to figure out where places were perspective,
so that way they could make decisions based on...
parks and i guess first nations rights and stuff
like that and where to start that initial conversations
are there other examples where it's being used
to inform policy instead of directly trying to
find the next deposit no i mostly people are
quite distrustful of this i feel like um it's
that there's a few like national efforts to to
do mineral prospectivity generally they're quite
simplistic and i don't quite know the use cases
for them i think I think generally the main driver
is coming from industry, from mines and from
that kind of brownfield, which means on mine
site kind of targeting. So you're saying perhaps
most of the real world case examples are locked
behind data vaults that never get published.
Exactly, yeah. What about other things that this
same technology can be used for? Like, they can't
be just for finding minerals. There's got to
be water resources. identifying things on the
Earth's surface? Yeah, we never reinvented the
wheel with this technique. We produced a novel
approach to it, but the actual technique is very
grounded in decades of research. So this is a
common thing in neuroscience and in medical sciences.
It's the same technology that you would use to
detect brain tumors, for example. You would train
the model to detect the... the causative link
between MRI images and brain tumors. And we actually
started this study with a proof of concept using
this malarial prevention data set, which was
satellite imagery, or aerial imagery, sorry,
of water tanks in Central America for the purpose
of mapping the locations where mosquitoes might
breed. And that was kind of our proof of concept
was identifying water tanks in satellite, sorry,
aerial data. This is a very kind of unknown technique.
And the novel thing is just kind of how we framed
that problem. So we just went from mineral prospectivity
to medical imaging to finding where mosquitoes
breed to help control malaria all in the same
sentence. Yeah. Yeah, a bit of a roller coaster.
No, but I mean, that just shows like how wide
this data or this technique or general science
behind the technique can be used for, right?
Like this is going to have real world implications.
Yeah, absolutely. And I think it will change
the world of mineral exploration eventually.
I don't know if it's quite there yet, but it's
something that will only grow over time. So before
we go to the traditional last question. All things
being equal, is there anything else you want
to share science -wise? A future trip, a paper,
a documentary suggestion? I don't know, is there
a new Spinosaurus documentary coming out? I don't
know, that would be cool. I mentioned the Spinosaurus
paper earlier. I recommend checking that out.
My latest extracurricular obsession has been...
something called bioacoustics which is uh studying
the the vocalizations of marine life because
i'm really into whale watching and so i've been
doing a lot of research into that and i might
be potentially getting involved in some research
in bioacoustics in the future which would be
very exciting but yeah i think that's a really
interesting emerging field being able to track
the location and movements of whales for conservation
purposes and for notifying ships of when there's
whales in the area and that kind of thing. Cool.
Yeah, we had someone on to talk about birds at
one point and being able to identify species,
although not yet individuals, from recordings
just out in the trees. So that's cool. Is that
like a citizen science thing? Exactly, yeah.
There's a couple of different citizen science.
organizations that work on that and again it's
using neural networks like we have in this study
to identify what whale vocalizations look like
and it requires that kind of building of the
training data for the predictive models. Is there
any group or anyone that might be listening in
the Vancouver area that they should get in contact
with if they want to join this citizen science
outreach? Yeah, there's a really cool organization
based in in Washington called Orca Sound. And
they're the main citizen science people working
on predictive models. And then there's a couple
more. The rest are kind of tied to universities.
So there's a research team at SFU called HALO.
And then there's also some guys at UBC and UVic
as well, all kind of working. They all kind of
work together. It's all a very collaborative
approach. So I think Orca HALO. The way science
should be. Yes, exactly. I think Orca Hello is
the main citizen science kind of approach. All
right. Now for the traditional last infamous
question. What is your favorite science joke?
Yeah, I've got one for your longtime listeners
that ties into the paper I mentioned earlier.
So my joke is a Spinosaurus walks into a bar.
The bartender asks him, why the long sail? Spinosaurus
says, you know, I have absolutely no idea. The
joke is that we have no idea what Spinosaurus...
What it used to say. What it did. I got it now.
Yeah, yeah. Takes a minute. Oh, boy. Normally,
I get it right away, or I feign polite indifference.
This time, I was like, no, no, I'm going to ask
because I really need to know. Okay, well, thank
you so much for being on. Yeah, thanks, Jeff.
It was a pleasure to be on, and I'd love to come
back sometime. Fantastic, but you'll need a new
joke. Yes, one that works immediately this time.
Well, that brings another episode to a close.
Thanks for joining so far. Remember, send some
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give the gift of science. Tell a friend. And
I'll see you in two weeks. so chaotic so misbehaved
echoes of melodies remind us it's always colors
weave stories painting the sky swaying to rhythms
as the galaxies fly by Deep in our bones Through
this melodic maze Our minds explore Change