戰慄
因恐懼、寒冷或激動而顫抖。晉˙羊祜˙讓開府表：「智力不可強進，恩寵不可久謬，夙夜戰慄，以榮為憂。」三國演義˙第三回：「厲聲問：『天子何在？』帝戰慄不能言。」亦作「顫慄」。
night・mare
nightmare scenario 戦りつのシナリオ.
讀到一篇科普：
Statistics and climatology
From The Economist print edition
其中幾個字眼想到三位朋友：
一是丁丁的：「解這首詩，關鍵字在「philosophy」，在濟慈那個時代，philosophy 一詞的字義包括 natural philosophy（自然哲學），何謂自然哲學？其實就是今謂「（自然／物理）科學」的舊稱，因此只要把 science 代入這詩裡的 philosophy，詩旨就很明白，豁然開朗了。」哲學非哲
這樣很了解第一段：
“SCIENCE” is a recently coined word. When the Royal Society, the world's oldest academy of the discipline, was founded in
末尾兩段：
Climate models have hundreds of parameters that might somehow be related in this sort of way. To be sure you are seeing valid results rather than artefacts of the models, you need to take account of all the ways that can happen.
That logistical nightmare is only now being addressed, and its practical consequences have yet to be worked out. But because of their philosophical training in the rigours of Pascal's method, the Bayesian bolt-on does not come easily to scientists. As the old saw has it, garbage in, garbage out. The difficulty comes when you do not know what garbage looks like.
其中的bolt-on ， 讓我想到螺絲大學的Justing 先生：
bolt sth on phrasal verb
to add an extra part or feature:
Other insurers will allow you to bolt on critical illness cover to standard life cover.
bolt-on
adjective [before noun]
added to a main product, service or plan as a smaller, extra part or feature, especially in business:
Mr Bungey said the business would continue to make bolt-on acquisitions.
看到這artefact，就知道是英國人寫法；美國人拼成 artifact。它在實驗上是一重要的字眼。我半年前從潘老師那兒學到的：
artefact MAINLY UK
noun [C] (MAINLY US artifact)
an object that is made by a person, such as a tool or a decoration, especially one that is of historical interest:
The museum's collection includes artefacts dating back to prehistoric times.
(from Cambridge Advanced Learner's Dictionary)
hc讀到潘震澤老師對「人為誤差（artifact）」的翻譯，覺得很有意思。他解釋如下：
*****
「短期實驗雖然乾淨俐落，但有許多潛在缺點：且不說處於麻醉中的動物是否能表現正常的生理，就連麻醉藥物及手術創傷都可能造成生理變化。因此，短期實驗的結果，不容易排出人為誤差（artifact）的影響。要想解決這個問題，生理學家必須進行較為「長期」（chronic）的實驗，也就是在動物身上先施以必要的手術及各種處理，等動物恢復且習慣之後，再於清醒狀態下進行觀察記錄。」（潘震澤：心理始於生理（二）－－帕甫洛夫與消化生理）
Artifact一般都譯成「人工製品」，但對研究人員而言，還有下面這個意思：
A spurious observation or result arising from preparatory or investigative procedures. (The Random House Unabridged Dictionary)
An inaccurate observation, effect, or result, especially one resulting from the technology used in scientific investigation or from experimental error. (The American Heritage Dictionary of the English Language)
A product of artificial character due to extraneous (as human) agency. (Merriam-Webster's Medical Dictionary)
譯成「人為誤差」或「儀器誤差」才更貼切。
****
hc謝謝潘老師。找個例：
Detecting Spatial Patterns in Biological Array Experiments
Spatial errors may cause experimental errors such as misidentifying active ... Identification of spatial artifacts depends on experiments being randomly
Detecting Spatial Patterns in Biological Array Experiments
根據你找的定義，artifact似乎是「人造物（設計）造成實驗觀查之誤判（導）」。我想，它與error屬於不同類，如果直接用「誤差」翻譯它，如下例，是否令人困惑？
Multiresolution Analysis of DEMs: Error and Artifact Characterization ... This assumption is confirmed by experiments both on simulated and on real
-----
HC：
你說的沒錯，artifact與 error 是有點不同，error 單純指的是偏離正確值的錯誤，而 artifact 一字特別強調實驗過程中，因實驗者及╱或儀器的問題，而出現原本不存在的結果，像是顯微鏡因鏡頭像差，出現原本不存在的影像、實驗紀錄上的「噪音（noise）」曲線或數值。所以「人為誤差」確實不是最貼切的翻譯，可能還是回歸「人為結果」好一些。 
震澤 於 2007/3/27 22:38 回應
Statistics and climatology
Gambling on tomorrow
Aug 16th 2007
From The Economist print edition
Modelling the Earth's climate mathematically is hard already. Now a new difficulty is emerging
“SCIENCE” is a recently coined word. When the Royal Society, the world's oldest academy of the discipline, was founded in London in 1660, the subject was referred to as natural philosophy. In the 19th century, though, nature and philosophy went their separate ways as the natural philosophers grew in number, power and influence.
Nevertheless, the link between the fields lingers on in the name of one of the Royal Society's journals, Philosophical Transactions. And appropriately, the latest edition of that publication, which is devoted to the science of climate modelling, is in part a discussion of the understanding and misunderstanding of the ideas of one particular 18th-century English philosopher, Thomas Bayes.
Bayes was one of two main influences on the early development of probability theory and statistics. The other was Blaise Pascal, a Frenchman. But, whereas Pascal's ideas are simple and widely understood, Bayes's have always been harder to grasp.
Pascal's way of looking at the world was that of the gambler: each throw of the dice is independent of the previous one. Bayes's allows for the accumulation of experience, and its incorporation into a statistical model in the form of prior assumptions that can vary with circumstances. A good prior assumption about tomorrow's weather, for example, is that it will be similar to today's. Assumptions about the weather the day after tomorrow, though, will be modified by what actually happens tomorrow.
Psychologically, people tend to be Bayesian—to the extent of often making false connections. And that risk of false connection is why scientists like Pascal's version of the world. It appears to be objective. But when models are built, it is almost impossible to avoid including Bayesian-style prior assumptions in them. By failing to acknowledge that, model builders risk making serious mistakes.
Assume nothing
In one sense it is obvious that assumptions will affect outcomes—another reason Bayes is not properly acknowledged. That obviousness, though, buries deeper subtleties. In one of the papers in Philosophical Transactions David Stainforth of Oxford University points out a pertinent example.
Climate models have lots of parameters that are represented by numbers—for example, how quickly snow crystals fall from clouds, or for how long they reside within those clouds. Actually, these are two different ways of measuring the same thing, so whether a model uses one or the other should make no difference to its predictions. And, on a single run, it does not. But models are not given single runs. Since the future is uncertain, they are run thousands of times, with different values for the parameters, to produce a range of possible outcomes. The outcomes are assumed to cluster around the most probable version of the future.
The particular range of values chosen for a parameter is an example of a Bayesian prior assumption, since it is derived from actual experience of how the climate behaves—and may thus be modified in the light of experience. But the way you pick the individual values to plug into the model can cause trouble.
They might, for example, be assumed to be evenly spaced, say 1,2,3,4. But in the example of snow retention, evenly spacing both rate-of-fall and rate-of-residence-in-the-clouds values will give different distributions of result. That is because the second parameter is actually the reciprocal of the first. To make the two match, value for value, you would need, in the second case, to count 1, ½, ⅓, ¼—which is not evenly spaced. If you use evenly spaced values instead, the two models' outcomes will cluster differently.
Climate models have hundreds of parameters that might somehow be related in this sort of way. To be sure you are seeing valid results rather than artefacts of the models, you need to take account of all the ways that can happen.
That logistical nightmare is only now being addressed, and its practical consequences have yet to be worked out. But because of their philosophical training in the rigours of Pascal's method, the Bayesian bolt-on does not come easily to scientists. As the old saw has it, garbage in, garbage out. The difficulty comes when you do not know what garbage looks like.
“Without ever invoking a spurious foresight of the Beethoven that was to come,” the newspaper wrote, “she placed the work in the 18th century, yet across the gulf that already separated him from Mozart.”
- (of a line of reasoning) apparently but not actually valid."this spurious reasoning results in nonsense"
- ARCHAIC(of offspring) illegitimate.
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