> Researchers want to be in specific locations because of their prestige. However, when all US-funded research is also available outside that location, the walled garden of prestige becomes rather porous. Especially since the reviewers typically aren't paid either.
You are assuming researchers are saintly figures dwelling in a vacuum who don't need to constantly prove to their department head or promotion evaluation committee of their worth. That is not the case. The walled gardens are desirable for some because their social functions are not easily replaceable.
One way to decouple the evaluation of scientific output from the walled garden is simply to stop using them as a gate-keeper in making hiring and research grant distribution decisions. But apart from the constant lip service, there is no momentum in doing anything concrete about this in academia.
> It’s mostly grunt work and whatever technical skills can be learned by a high school student over a summer.
Who do you think is supervising the high school student? Where does the idea for the project come from? Where does the money supporting the high school intern' experiments come from?
It isn't remotely true. Corals have persisted through the geological past when atmospheric CO2 was many times today's level. It could be true that _some_ coral species would go extinct, but corals as a class are likely to persist.
> Corals have persisted through the geological past when atmospheric CO2 was many times today's level.
We're heading toward something similar to the Miocene, both in CO2, temperature, and ph. You might want to look into the "Middle Miocene disruption", which refers to a wave of extinctions of terrestrial and aquatic life forms that occurred following the Miocene Climatic Optimum. There's a big oceanic fossil gap in the Miocene, because of this peak-temperature which seemed to tilt ecosystems too far. We do know the hard-coral scleractinians (same as modern corals^1) didn't appear because of this change, but likely because they were one of the few types that can survive it. Unfortunately, if the trend continues, the hardiest of the ancient species all die^2. 7.2 doesn't sound like 7.95? The ocean isn't uniformly affected^3 and will likely kick off another "disruption" of the existing ecosystems causing a collapse. ie You don't need to set fire to all the trees for the forest to burn down.
Ugh, smells like crank. Someone discovered a climatology 101 fact and then made a big fuss about it. Just ... read an introductory textbook on climate science please. It's basically all speculation disguised as a "case study". The authors should show some numbers if they are really serious about their claim, for example, how much increase in marine evaporation can be expected from an idealized sterile ocean.
I think it depends on whether you use it for operations or for data analysis. Speed is only one concern and it may not always be the most relevant concern.
A statistician/data scientist wrangling data and making plots would not have cared whether loading a CSV file takes one second or one microsecond, because they may only do it a handful of times for a project.
A data engineer has different requirements and expectations. They may need to implement an operational component that process CSV files repeatedly for billions of time a day.
If your use case is the latter, then pandas is probably not for you.
I love pandas and work with quite small datasets for EDA (10^(3..6) most of the time) but even then I run into slowdowns. I don't really mind as I'm pursuing my own research rather than satisfying an employer/client, and often figuring out why something is slow turns into a useful learning experience (the canonical rite of passage for new pandas users is using lambda functions with df.apply instead of looping).
I've definitely procrastinated doing some analyses or turning prototypes into dashboards because of the potential for small slowdowns to turn into big slowdowns, so it's nice to have other options available. I'm very interested in Dask but have also been apprehensive about doing something stupid and incurring a huge bill by failing to think through my problem sufficiently.
I don't think you fully appreciate that what you talk about are several different aspects of the climate system and they are true at the same time. Which one is the more salient factor at play depends on the time scale you look at.
> fossil-fuel-sourced CO2 and CH4 are steadily warming the planet
No climate scientist denies this. It is the trend happening since the industrial revolution. But say, if you have three months or three years of data, you are not gonna be able to fit a statistically robust trend. At best, you can do some Bayesian attribution of the likelihood that a climate event is attributable to the long-term climate change effect.
> Many of the supposed discoveries of periodic oscillations in the North Atlantic and the Pacific simply don't hold up over time, and as with ENSO, have zero predictive power over what phase/amplitude of the next 'period of the oscillation' will be.
You are jumping into conclusions too soon. The fact that a system has periodicity does not necessarily mean that it is perfectly deterministic. You may not be able to predict when the next El Niño is gonna happen, but you can say with confidence how many El Niño events you will likely see in a 100-year time span.
> These phenomena are likely mostly chaotic in nature
Again, being chaotic does not mean no predictability at all. The atmosphere and the ocean do follow the basic laws governing fluid dynamics. How much predictability you have very much depends on the spatial and temporal scale you are looking at. Being able to predict one event and being able to predict a statistical distribution or a power spectrum are two different things.
The title seems to imply that this problem does not exist in languages not using Latin scripts. That's not true. Take Japanese for an example, when you have hiragana, katakana, and kanji to express the same idea (and not to mention a kanji typically have more than one way of pronouncing it), the problem is gonna be orders of magnitude more complex.
> Take Japanese for an example, when you have hiragana, katakana, and kanji to express the same idea
I'm sure you know this, but for readers who aren't familiar with Japanese, it might be worth saying that hiragana, katakana and kanji aren't strictly interchangeable. Sure you can use hiragana and katakana as a fallback when you cannot or don't want to use the kanji for various reasons, but normally in every situation there's a recommended script to use in order to write correct Japanese.
I think the idea is to think about what would have happened if programming started in another language. If it were Japanese, it’s easy to imagine that you’d have the same issue with different styles in different contexts.
Some contexts might call for all hiragana variables, some all katakana, and some using the most likely/appropriate form of the word, including kanji. And in the third case, you’re still going to end up with discrepancies since not everyone agrees on when to use kanji.
And don't forget script number four in Japanese: Latin letters! Used for all sorts of odds and ends not covered by the other scripts; mostly (abbreviations of) names like 「ＩＢＭ」. These conveniently come in full-width versions for use with kanji, hiragana, and katakana, but this of course won't stop anyone from using the basic letters we're using here. So in the hypothetical case where Unicode is a thing, but the lingua franca of software engineering is Japanese instead of English, you might end up with variables named:
変数
へんすう
ヘンスウ (or even ﾍﾝｽｳ)
ｖａｒｉａｂｌｅ
ＶＡＲＩＡＢＬＥ
variable
VARIABLE
…and all the additional snakes and camels varying the notations further.
