jofer

Thankfully, it's a fairly deep (>100km) subduction zone earthquake. That means less immediate damage from ground acceleration than it could be from an earthquake of that size. However, a very large area may have seen significant liquefaction of soils in valleys and landslides on steep slopes. I'd wager we won't hear about most of the damage initially in the press.

Yes. My point was that LLMs aren't currently good for everything. The original commenter literally said they were good at everything and I offered a counterpoint of something they're not good at: Most science.

(And yes, a lot of science is software. Analysis is software.)

Just for some context, the domain we're talking about is oil and gas and mineral exploration. E.g. At my previous job, I used to personally manage a >$400 million per year budget and that wasn't even considered significant. We had multiple >$10 billion per year projects ongoing. That was 10 years ago. The amounts are larger now.

The issue isn't a lack of economic interest.

It might be a lack of training data in addition to inherent complexity, but it's certainly not a lack of economic interest.

Try working in anything domain specific outside of common CRUD patterns. E.g. scientific software development where you describe a problem + give data. I have yet to see a single example of feeding in a problem in natural language involving a specific scientific domain that wasn't pretty catastrophically incorrect.

But yeah, if you want to feed it math and get code, it's reasonably okay with that. All LLMs I've used seem bad at understanding things that don't look like broad human knowledge. I've seen this same general issue across many different models. (And to be fair, geology, geophysics, and remote sensing are what I'm testing, and their semi-rare niches.)

It's also quite dangerous because it's not obvious that what it's doing is complete hallucinations unless you actually are a domain expert. Things _sound_ reasonable. E.g. "this is likely feature X" which _does_ exist, but is absolutely _not_ relevant to the problem or present in the input dataset.

But my current employer is pushing this exact thing (human language + scientific data + LLM -> advanced analysis of scientific data by LLM -> business decisions) and it _really_ worries me. It often gives the rough equivalent of "Start the procedure by severing the patient's aorta. Once they stop moving, you can deal with the hangnail". Just in very reasonable sounding language. And a lot of people don't know any better, because most users aren't domain experts.

It's not microplastics coming from the gloves. It's particles of the powder used to coat the gloves and keep them from sticking. Different composition, but similar and easily mistaken.

Huh. I weirdly enough have worked with a lot of those sites from the remote sensing side, but never really know what the overall project was. Just "use the NEON sites for examples". I should have looked it up more at the time. Thanks for sharing!

I also have spent quite awhile as an exploration geophysicist. I miss it! I work purely with satellite data now, which is decidedly less tangible.

I've done a fair bit in the field, but a huge part of my career has been mining old datasets and reinterpreting things in light of new data/etc.

What the article is describing isn't new in any way. But it also doesn't remove the need for fieldwork or the need for the experience of having done fieldwork to use existing datasets. Observational sciences (e.g. geology, biology, etc) where you can't easily replicate the environment you are studying in the lab are always going to hinge on some sort of fieldwork.

Finding creative ways to use existing data doesn't change that.

It may not be immediately obvious to folks outside of geoscience, but the main way something like this is useful is as a measure/metric to compare things. Looking at the number of faces of fractured pieces isn't normally something we do often in geology.

Sure, the pieces average 6 faces when materials are relatively homogenous and iostropic (i.e. no preferential direction to break in and no free surface nearby). However, as they note in the article, this isn't always the case. Things like mud flats and other cases with very anisotropic materials and/or free surfaces nearby don't fracture with the same average.

This is a good example of a potential metric that could be used to give some clues about overall material behavior even if all you have are the broken remains.

Fractal dimension is also pretty esoteric. However, it's somewhat widely used in geoscience, even though what we're measuring isn't _actually_ fractal. It's still a very useful comparative metric, though, because it lets us measure how complex an interface or surface is quantitatively and scale-independent.

It's still quite controversial whether or not they produce oxygen that way. It's been hypothesized, but I wouldn't consider it a consensus or settled. There are also microbes that can produce oxygen without light, so there are other mechanisms to explain "dark oxygen" in deep sea ecosystems.

With that said, the simple truth of it is that we know next to nothing about these ecosystems and really can't accurately estimate impacts. They're quite possibly significant, but we just don't have much info to go off of and studies like this are sorely needed.

That exactly. It's not that it's impossible. It's that it's heavy to efficiently transport heat to the radiators or requires a lot of tiny sats, which have their with problems.

What really worries me is that I keep hearing "cooling is cheap and easy in space!" in a lot of these conversations, and it couldn't be farther from the truth. Cooling is _really_ hard and can't use efficient (i.e. advection-based air or water cooling) approaches and are limited to dramatically less efficient radiative cooling. It doesn't matter that space is cold because cooling is damned hard in a vacuum.

The article makes this point, but it's relatively far in and I felt it was worth making again.

With that said, my employer now appears to be in this business, so I guess if there's money there, we can build the satellites. (Note: opinions my own) I just don't see how it makes sense from a practical technical perspective.

Space is a much harder place to run datacenters.

Ah, fair enough! I think I misread some things around the library initially awhile back and have been making incorrect assumptions about it for awhile!

Good point, but I think we're talking past each other a bit.

Typing in python within the scientific world isn't ever used to check types. It's _strictly_ only documentation.

Yes, MyPy and whatnot exist, but not meaningfully. You literally can't use them for anything in this domain (they wont' run any of the code in question).

Types (in this subset of python) are 100% about documentation, 0% about enforcement.

We're setting up a _documentation_ system that can't express the core things it needs to. That worries me. Setting up type _checks_ is a completely different thing and not at all the goal.