生物与医学2020elifesciences.org
自工业革命以来美国人体温下降
Decreasing human body temperature in the U.S. since the industrial revolution
进阶 · 约 45 分钟读完前置知识：基础统计学/回归分析、体温与代谢的生理学基础、历史队列数据解读
导读
这篇论文研究美国人群平均体温是否自工业革命以来发生变化，并分析跨越大时间范围的历史体温记录。结果显示平均体温呈长期下降趋势，约每十年下降0.03°C；作者进一步讨论代谢率等生理因素的可能作用。
HN 关注点：这种长期下降是真实生理变化，还是测温仪器、记录方式和统计方法造成的偏差。
HN 评论精选
这几条评论里：质疑 2 补充 2
质疑用费曼的测量偏差故事提醒：体温长期下降可能是测量或预期偏差，而非真实生理变化。
随时间下降。费曼著名演讲《货物崇拜科学》中的例子很相关：电子电荷的后续测量长期被密立根的错误值“锚定”，偏离太大的结果常被当成错误。这里未必是同样情况，但在下结论（说实际体温变了，而非仪器或记录方式变化等）前，需要先排除这类偏差。没看过的话，整个演讲值得花5分钟。
harry8查看英文原文
Decreasing over time. The following example from Feynman's rightly extremely famous "Cargo Cult Science" lecture is extremely relevant. Now it may not be what is going on here at all, but that probably needs to be established somehow before deciding that some other alternative (eg instrumentation changes, everyone's actual temperature has changed rather than just its recording, whatever other theory). And if you haven't seen it, it's fantastic. As is the whole lecture, 5 minutes you really won't regret. "We have learned a lot from experience about how to handle some of the ways we fool ourselves. One example: Millikan measured the charge on an electron by an experiment with falling oil drops and got an answer which we now know not to be quite right. It’s a little bit off, because he had the incorrect value for the viscosity of air. It’s interesting to look at the history of measurements of the charge of the electron, after Millikan. If you plot them as a function of time, you find that one is a little bigger than Millikan’s, and the next one’s a little bit bigger than that, and the next one’s a little bit bigger than that, until finally they settle down to a number which is higher. Why didn’t they discover that the new number was higher right away? It’s a thing that scientists are ashamed of—this history—because it’s apparent that people did things like this: When they got a number that was too high above Millikan’s, they thought something must be wrong—and they would look for and find a reason why something might be wrong. When they got a number closer to Millikan’s value they didn’t look so hard. And so they eliminated the numbers that were too far off, and did other things like that." [1] Needless to say the actual charge on an electron is very likely to have been constant over that period rather than trending steadily upward. [1] http://calteches.library.caltech.edu/51/2/CargoCult.htm
质疑质疑几十年前后温度计精度和校准一致性，担心系统测量误差影响长期趋势。
讨论长期内相对小的温度变化时，我对这些测量的置信度存疑。像这样非常规律的长期趋势至少说明随机噪声没有压倒信号，但我很难相信我们制造极精确温度计（测到百分之一度）的能力在几十年里一直如此稳定。我错了吗？我也常想这如何影响气候变化研究。海洋酸化在我看来科学上更扎实，因为效应更可见，但我也不确定我们几十年精确测 pH 的能力。
TallGuyShort查看英文原文
When we're discussing relatively small changes in temperature over very long periods of time, I wonder how confident we are in those measurements? I mean a very regular trend like this is a pretty strong signal that you're at least not overwhelmed by random noise, but: I find it very hard to believe that our ability to manufacture very precise and accurate thermometers has been so consistently good (measuring hundredths of degrees) for the order of decades. Am I wrong? I often wonder how much this impacts climate change studies as well. Ocean acidification just "feels" like much sounder science to me because the effects are so much more visible, but I'm not certain about our ability to measure pH so precisely for decades either.
补充指出体温变化可作为基础代谢率代理，关联寿命和体型，所以有生物学意义。
从引言看，为什么有趣：平均体温是否随时间变化并非只是好奇。人体温度是基础代谢率的粗略替代指标，而基础代谢率又与寿命（代谢率更高，寿命更短）和体型（代谢更低，体重更大）相关。
badfrog查看英文原文
From the intro, why this is interesting: > The question of whether mean body temperature is changing over time is not merely a matter of idle curiosity. Human body temperature is a crude surrogate for basal metabolic rate which, in turn, has been linked to both longevity (higher metabolic rate, shorter life span) and body size (lower metabolism, greater body mass).
补充指出统计学家 Trevor Hastie 参与，增加对方法严谨性的关注。
有趣的是，Trevor Hastie 是这篇论文的作者之一。这里的人大概最熟悉他合写的书：《统计学习要素》（2001）和《统计学习导论》（2013）。
ivanech查看英文原文
An interesting note: Trevor Hastie is an author on this paper. The crowd around here probably knows him best for books he co-wrote: The Elements of Statistical Learning (2001) and An Introduction to Statistical Learning (2013).
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