Remember that hockey stick?Follow

No. If the air reaches a Sweating Point above 35 C, the human body will not be able to maintain it's Core body temperature of 37.0 °C (98.6 °F). You'll take a couple of steps past your front porch, pass out, and then rapidly die of heat trauma to the brain.

You got a cite for this? Google isn't giving me much. I found some stuff on heat-stroke and related stuff, but that's all.

The top 6 to 8 climate laboratories in the world can and are doing it. They have access to all the world-wide data on the matter, and the present day best super-computers and specialist programs to run backwards scenarios to confirm their calculations conform with past data. When they run forward calculations, the 6-8 labs world-wide output extremely similar outputs over time, when fed in the present level of CO2 and other climactic modifiers in the world. Also extremely similar outputs when fed in various hypothetical CO2 rises beyond the present, which are deemed worthy to chew up that amount of computing power, because of the various likelihoods of future CO2 rises in the atmosphere from human activity.

If an individual measurement has a 2 degree margin of error, but you take thousands of measurements (assuming the measurements are randomized, not all from the same location and therefore suffering from the same error polarity), then you are still going to end up with an accurate mean.

I state this as a general property of statistics and without any knowledge of the details of your guys' arguments.

Story time!

My first lead-author paper got heavily cited by another paper that came out probably 3 years later. They were doing an analysis where they used some of the my data to put together a new method for quantitatively measuring protein abundance in complex mixtures; you know error bars and the like, etc.

The funny thing is they never bothered to contact me before using my data. If they had, I would have warned them we had thought of doing the same thing initially, but after looking over the data decided it was too inconsistent to be used quantitatively. In short if you want to average results from many analyses it helps if you do the experiment the same way every time... . It was no skin off our backs at the time. We were just trying to put out a dataset for testing software, and a quantitative analysis was thought up halfway through. It was too late to really mess with the idea without starting over at that point, so we ditched the idea.

So here I am reading a paper that uses this data in a way it was never meant to be used. Worse yet, it's in a Nature journal, so not exactly some backwater publication nobody really reads, where it could simply get passed over or ignored. They didn't do anything to correct for some of the errors that were there, and there were inconsistencies they flat-out ignored that made the data do funny things (which they, of course, didn't bother to explain). Not to mention there were other problems, but they're not of the nature I can probably explain here, basically misusing parameters and what not. In short it was a ************

I think it was somewhere around that time I lost my blind faith in anyone outside of a mathematical field doing math right, not to mention the peer-review process in general. In retrospect I partly wish I had just done the analysis with that data, it was obviously good enough to get into a nice journal and I would have at least known what errors were in the data when I reported it... At the same time I'm glad my name isn't anymore associated with that debacle than it is already.

Averaging data from different analyses isn't something that can really be done lightly. I mean you can't just do 3 different tests and take a mean or something. Error propagation is a homely little *******, and of course there's no way to combine some data (not that that stops anyone...), and those pesky systematic errors get glossed over when someone starts bragging about some p-value with an exponent large enough to make a mistake impossible within the lifespan of the universe. No one bothers to say "this measurement is perfectly accurate assuming the following conditions..."

I guess in the end I still trust the experts though, just not as blindly as before. If nothing else it made me want to learn more about doing some of these calculations correctly, and what that meant. I do have a lot less blind faith in numbers people report, but sometimes I wish I knew more about how to double-check them. Eventually these kinds of things fall apart if they're done wrong, just gotta give time for the cream to rise to the top and what not I guess. Always takes longer than we want it to.

TL:DR = Scientists are people too apparently.

I can dig up a hundred papers by PhD's who share my viewpoint if you really wanted. I'm not going to, but there are dissenting viewpoints out there on the scope and accuracy of super early climatological modeling regarding accuracy and assumed variables within epoch level timeframes. I always find it amusing when other people assume a single citizen doesn't have enough knowledge to seek experts on the subject and know what they are talking about on a given subject.

It's also the point of examining the studies in aggregate; seeing what multiple bodies are determining using different studies, different data sets, etc. A bit like polling in a sense -- maybe we need to unskew the temperatures.

That's how it is supposed to work, yes. Of course it still works that way in practice to some degree. But there's problems with the system that are undermining the peer-review process. Really it's buggy at best right now.

First, there's just a lot of science out there right now. We're victims of our own success in a sense. Reading and understanding papers is hardly easy, even for someone familiar with the field. You could read several relevant papers a day and still not cover enough many times. There's a general ignorance among scientists to some extent, and in many ways it's not really our fault. There's just too much out there to keep up with.

Also, when a paper gets submitted you're having 2-3 "experts" look it over for accuracy. Expert simply meaning someone who's published with the journal in the past in many cases. These people will likely get drawn from different areas, so you'll end up with a single person spending a couple of hours leafing through the statistics side of your paper. Chances are s/he may have never done the same kind of analysis you're doing, they've simply done the math for a paper published in your field. Even more fun is when you get to suggest people to review your article, it's a popular thing these days as it saves the journal the trouble of trying to track down an expert.

As for duplicating someone else's results? It's not like anyone sits down and repeats an experiment someone else did. If you can use someone else's methods to get published, well that's awesome, but it's not like these things are being rigorously tested. If someone fails to reproduce results, no one will likely hear anything of it. Really aren't you just saying you're a failure at that point if someone else does it successfully? That kind of thing isn't really publishable in-and-of itself anyway. There's no novel aspect to the study, and you'd be hard-pressed to find a journal that would accept it. At that point you're probably collaborating with someone outside your field, and your interesting critique of some computer algorithm gets buried in an article in Neuron or something.

In the end though, a lot of it comes down to time as well. A Ph.D. will spend around 50% of their time writing. Applying for grants is a huge timesink, and you need all the savvy of a Business Entrepreneur to be successful. Can you sell your idea? How much do you have to hype it to get funded? A successful scientist is a successful salesman, first and foremost. Of course, where better to get your ideas than the grant proposals you're helping to review? There's many an instance of papers and grants getting rejected only to see a suspiciously similar thing get accepted not long after. Because of this you learn how to be a little vague on the methods. Enough information to let people know what you did, not enough to let them steal your idea. If absolutely necessary, relevant details can always be stuffed in a table the supplemental section somewhere where no one will ever see it.

Anyway I'm writing too much again. The process still largely works of course, but between a lot of money, a lot of competition, and a just a lot of information it's very difficult to get the system to function the way we'd like it to.

Just one person's opinion of course.


Edited, Mar 12th 2013 8:53am by someproteinguy

To give your rant some sort of proof: Diederik Stapel altered and falsified data for years and years

"Diederik Alexander Stapel (born 19 October 1966 in Oegstgeest, western Netherlands), is a former professor of social psychology at Tilburg University and before that at the University of Groningen in the Netherlands.[1] In 2011 Tilburg University suspended Stapel, pending further investigation, for fabricating and manipulating data for his research publications. This scientific misconduct took place over a number of years and affected at least 55 publications.[2]"

"The report concludes that there was "a more general failure of scientific criticism in the peer community and a research culture that was excessively oriented to uncritical confirmation of one’s own ideas and to finding appealing but theoretically superficial ad hoc results". It goes on to suggest that "not infrequently reviews [of social psychology journal articles] were strongly in favour of telling an interesting, elegant, concise and compelling story, possibly at the expense of the necessary scientific diligence."[27]"


Obviously, this is in a different field but that someone has been able to falsify data for years and got through at least 55 peer reviews (including one for Science Magazine) without this being detected before finally being caught by some of his students does point to more than some small issues in the peer reviewing process.

Edited, Mar 12th 2013 7:41pm by Aethien

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His hoax lasted for something like 18 months after being published. According to this article, he published in 2004 & 2005 and, by 2006, was stripped of his license by the S. Korean government.

So I suppose for certain definitions of "good while", it's true but not really indicative of your "20-30 years" statement later on.

Sorry seems my brain shifted gears mid-thought there, self correcting not just in a fraud sense but a general sense too. Where we thought we understood something, but it later turned out we didn't, or we only got it partly right. Not realizing until years later.

Edited, Mar 12th 2013 12:02pm by someproteinguy