翻译一篇reddit上讨论”基因熵“的发言。
大体而言，一个科学理论需要做出”惊奇“而可以验证（证伪）的预测，并通过实验的检验。这是科学哲学的基本素养。”fit evidence in“是一种逻辑诡辩，暂时不予讨论。
我的生物学知识已经过期作废，术语也生疏了。翻译若有错误，都算DeepL。
顺便说一下，DeepL把”Genetic Entropy“ is BS中的”BS“翻译为胡说八道（大概它以为这是Bullshit的缩写），我是不赞成的。BS = Biblical Science，不接受反驳。
“Genetic Entropy” is BS: A Summary
“基因熵 “是一种胡说八道：摘要
翻译：DeepL
瞎参合：Eddy
The idea of “genetic entropy” is one of a very few “scientific” ideas to come from creationists. It’s the idea that humanity must be very young because harmful mutations are accumulating at a rate that will ultimately lead to our extinction, and so we, as a species, can’t be any older than a few thousand years. Therefore, creation. John Sanford proposed and tried to support this concept in his book “Genetic Entropy & The Mystery of the Genome,” which is…wow it’s bad. EDIT: If you want to read “Genetic Entropy,” you can find it here (pdf). It’s a quick read, and probably worth the time if you want to be familiar with the argument. Might as well get it from the source. “基因熵”的概念是来自创造论者的极少数 “科学”概念之一。它认为人类必定非常年轻，因为有害突变的积累速度最终会导致我们的灭绝，所以我们作为一个物种，不可能比几千年前更老。因此，创造科学家约翰-桑福德（John Sanford）在他的《退化论：基因熵与基因组之谜》（Genetic Entropy & The Mystery of the Genome）一书中提出并试图支持这一概念，这本书是……哇，真糟糕。编辑：如果你想读《退化论》一书，你可以在这里找到它（pdf）。这本书很通俗易懂，如果你想熟悉这个论点，花时间浏览一下，了解来龙去脉还是值得的。
Everything about the genetic entropy argument is wrong, including the term itself. But it comes up over and over and over, including here, repeatedly, I think because it’s one of the few sciencey-sounding creationist arguments out there. So join me as we quickly cover each reason why “genetic entropy” is BS. 关于基因熵论的一切都是错的，包括这个词本身在内。但是它却一次又一次地被人提及，包括在这里也反复出现。我想，这是因为它是少数几个听起来很科学的创造论者“原创“论点之一。所以，请和我一起，快速地了解一下”基因熵 “错误的原因。
I’m going to do this in two parts. First we’ll have a bunch of quick points, and after, I’ll elaborate on the ones that merit a longer explanation. Each point will be labeled “P1”, “P2”, etc., as will each longer explanation. So if you want to find the long version, just control-f the P# for that point. 我将分两部分进行。首先，我们将有快速给出一组论点，之后，我将对那些值得仔细解释的观点进行阐述。每个要点将被标记为 “P1″、”P2 “等，每个较长的解释也是如此。因此，如果你想找到长的版本，只要用control-f调出搜索，并输入编号P#即可。
P1: “Genetic entropy” is a made-up term invented by creationists to describe a concept that already existed: Error catastrophe. Even before it’s a vaguely scientific idea, the term “genetic entropy” is an attempt at branding, to make a process seem more dangerous or inevitable through changing the name. I’m going to use the term “error catastrophe” from here on when we’re talking about the actual population genetics phenomenon, and “genetic entropy” when talking about the silly creationist idea.
P1: “基因熵 “是创造论者捏造的术语，用来描述一个已经存在的概念——错误灾难（Error catastrophe）。甚至在它成为一个模糊的科学概念之前，”基因熵 “这个术语就是一种宣称手段，使徒通过改变名称，使一个过程看起来更加危险或不可避免。从现在开始，当我们谈论实际的群体遗传学现象时，我将使用 “错误灾难 “一词，而当谈论愚蠢的创造论思想时，我将使用 “基因熵”。
P2: Error catastrophe has never been observed or documented in nature or experimentally. In order to conclusively demonstrate error catastrophe, you must show these two things: That harmful mutations accumulate in a population over generations, and that these mutations cause a terminal decline in fitness, meaning that they cause the average reproductive output to fall below 1, meaning the population is shrinking, and will ultimately go extinct.
This has never been demonstrated. There have been attempts to induce error catastrophe experimentally, and Sanford claims that H1N1 experienced error catastrophe during the 20th century, but all of these attempts have been unsuccessful and Sanford is wrong about H1N1 in every way possible.
P2：在自然界或实验中，从未观察到或记录过“错误灾难”。为了确凿地证明“错误灾难”存在，你必须证明这两件事。（1）有害的突变在一个种群中世代积累，而且这些突变会导致适应度致命性降低，也就是说，它们会导致平均繁殖力低于1（这意味着种群正在萎缩，并最终灭绝）。
这一点从未被证明过。有人试图通过实验诱发错误灾难，桑福德声称H1N1在20世纪经历了错误灾难——但所有这些尝试都不成功，并且桑福德在各个方面对H1N1的描述都是错误的。
P3: The process through which genetic entropy supposedly occur is inherently contradictory. Either neutral mutations are not selected against and therefore accumulate, or harmful mutations are selected against, and therefore don’t accumulate. Mutations cannot simultaneously hurt fitness and not be selected against.
P3: 所谓的基因熵发生过程本质上是矛盾的。要么选择不排除中性突变，因此中性突变累积；要么选择排斥有害突变，因此有害突变不累积。突变不可能同时损害适应性，又不被选择所排除。
P4: As deleterious mutations build up, the percentage of possible subsequent mutations that are harmful decreases, and the percentage of possible beneficial mutations increases. The simplest illustration is to look at a single site. Say a C mutates to a T and that this is harmful. Well now that harmful C–>T mutation is off the table, and a new beneficial T–>C mutation is possible. So over time, as harmful mutations accumulate, beneficial mutations become more likely.
P4：随着有害突变的积累，后代可能发生有害突变的百分比减少，发生有益突变的百分比增加。最简单的说明是看单点基因。假设一个C突变为一个T，成为有害基因。现在有害的C–>T突变不存在了，而新的有益的T–>C突变则成为可能。所以随着时间的推移，随着有害突变的积累，有益突变的可能性会提高。
P5: (Somewhat related to P4) A higher mutation rate provides more chances to find beneficial mutations, so even though more harmful mutations will occur, they are more likely to be selected out by novel beneficial genotypes that are found and selected for. This is slightly different from P4, which was about the proportion of mutations; this is just raw numbers. More mutations means more beneficial mutations.
P5：（与P4有点关系）较高的突变率提供了更多发现有益突变的机会，因此即使会出现更多的有害突变，它们也更有可能被发现和选择的新型有益基因型所淘汰。这与P4主张的突变比例略有不同。P5是纯粹数字计算——更多的突变意味着更多的有益突变。
P6: Sanford is dishonest. His work surrounding “genetic entropy” is riddled with glaring inaccuracies that are either deliberate misrepresentations, or the result of such egregious ignorance that it qualifies as dishonesty.
Two of the most glaring examples are his misrepresentation of a distribution of fitness effects produced by Motoo Kimura, and his portrayal of H1N1 fitness over time.
P6：桑福德是个不诚实的人。他围绕 “基因熵 “所做的工作遍布明显的误导和不准确。这些不准确之处要么是故意歪曲，要么是由于极度无知而导致的不诚实行为。
其中最明显的两个例子是他对Motoo Kimura提出的适应效果分布的歪曲，以及他对H1N1适应效果随时间变化的错误描述。
Below this point you’ll find more details for some of the above points. 下面是对上述观点的详细说明。
P2: Error catastrophe has never been observed, experimentally nor in nature. There have been a number of attempts at inducing error catastrophe experimentally, but none have been successful. Some work from Crotty et al. is notable in that they claimed to have induced error catastrophe, but actually only maybe documented lethal mutagenesis, a broader term that refers to any situation in which a large number of mutations cause death or extinction. Their single round of mutagenic treatment of infectious genomes necessarily could not involve mutation accumulation over generations, and so while mutations my have caused the fitness decline, it isn’t wasn’t through error catastrophe. It’s also possible the observed fitness costs were due to something else entirely, since the mutagen they used has many effects.
J.J. Bull and his team have also worked extensively on this question, and outline their work and the associated challenges here. In short, they were not able to demonstrate terminal fitness decline due to mutation accumulation over generations, and in one series of experiments actually observed fitness gains during mutagenic treatment of bacteriophages.
P2：无论是在实验中还是在自然界中，都没有观察到错误灾难的发生。在实验中曾有过一些诱导错误灾难的尝试，但都没有成功。Crotty等人的工作值得注意——他们声称诱发了错误灾难，但实际上可能只是记录了致命的诱变（lethal mutagenesis）。这是一个更广泛的术语，指的是大量突变导致死亡或灭绝的情况。他们对感染性基因组的单轮诱变处理不可能涉及历代的突变积累，因此，虽然突变可能造成了适应度下降，但这并不是错误灾难的效果。他们观察到的适应代价完全可能由于其他原因造成的，因为他们使用的诱变剂有许多副作用。
J.J. Bull和他的团队也对这个问题进行了广泛的研究，并在此概述了他们的工作和相关挑战。简而言之，他们无法证明突变的世代积累会导致的最终适应度下降，甚至在一系列的实验中，实际上观察到对噬菌体进行诱变处理期间适应度的增加。
You’ll notice that all of that work involves bacteriophages and mutagenic treatment. What about humans? Well, phages are the ideal targets for lethal mutagenesis, especially RNA and single-stranded DNA (ssDNA) phages. These organisms have mutation and substitution rates orders of magnitude higher than double-stranded DNA viruses and cellular organisms (pdf). They also have small, dense genome, meaning that there are very few intergenic regions, most of which contain regulatory elements, and even some of the reading frames are overlapping and offset, which means there are regions with no wobble sites.
This means that deleterious mutations should be a higher percentage of the mutation spectrum compared to, say, the human genome. So mutations happening faster plus more likely to be harmful equals ideal targets for error catastrophe.
你会注意到，所有这些工作都涉及噬菌体和诱变处理。那人类呢？好吧，噬菌体是致命诱变的理想目标，特别是RNA和单链DNA（ssDNA）噬菌体。这些生物体的突变和替代率比双链DNA病毒和细胞生物体高几个数量级（pdf）。它们的基因组也小而密集，这意味着基因间隔区域非常少，其中大部分包含调控元素，甚至有些阅读框（reading frame）是重叠和偏移的，这意味着有些区域没有摇摆位点(wobble sites)。
这意味着，与人类基因组相比，噬菌体de 有害突变在突变谱系中的比例应该更高。因此，噬菌体的突变发生得更快，更可能是有害突变——错误灾难的理想目标。
In contrast, the human genome is only about 10% functional (<2% exons, 1% regulatory, some RNA genes, a few percent structural and spacers; stuff with documented functions adds up to a bit south of 10%). It’s possible up to 15% or so has a selected function, but given what we know about the rest, any more than that is very unlikely. So the percentage of possible mutations that are harmful is far lower in the human genome compared to the viral genomes. And we have lower mutation and substitution rates.
相比之下，人类基因组只有大约10%的功能性基因（<2%的外显子，1%的调节性基因，一些RNA基因，百分之几的结构和间隔物；已知的功能性基因加起来也就10%左右）。基因中可能有高达15%或更多部分具有选择性功能，但考虑到我们对其余部分的了解，超过这个比例的可能性非常小。因此，与病毒基因组相比，人类基因组中可能出现的有害突变比例要低得多。而且我们的突变和替换率也较低。
All of that just means we’re very unlikely to experience error catastrophe, while the viruses are the ideal candidates. And if the viruses aren’t susceptible to it, then the human genome sure as hell isn’t.
所有这些证据意味着我们非常不可能经历错误灾难，而病毒则是错误灾难实验的理想候选人。如果病毒都不容易受到影响，那么人类基因组更加不会。
But what of H1N1? Isn’t that a documented case of error catastrophe. That’s what Sanford claims, after all.
但是H1N1是怎么回事？这不就是一个有记录的错误灾难案例吗。这毕竟是桑福德声称的。
Except yeah wow that H1N1 paper is terrible. Like, it’s my favorite bad paper, because they manage to get everything wrong. Here’s a short list of the errors the authors commit:
除了一件事，哈，那篇H1N1论文写得很糟糕。这是我最喜欢的一篇糟糕论文，因为他们设法把所有的事情都弄错了。以下是作者所犯错误的一个简短清单：
They ignored neutral mutations.他们忽视了中性变异。
They claimed H1N1 went extinct. It didn’t. Strains cycle in frequency. It’s called strain replacement.他们宣称H1N1灭绝了。实际上不是这么回事。菌种循环的频率很高。这就是所谓的菌种替换（strain replacement）。
They conflated intra- and inter-host selection, and in doing so categorize a bunch of mutations as harmful when they were probably adaptive. 他们把宿主内部和宿主之间的选择混为一谈，于是把一堆突变都归为有害，而这些突变可能是增加适应性的。
They treated codon bias as a strong indicator of fitness. It isn’t. 他们把密码子偏向（codon bias）作为适应性的有力指标。其实不然。
Translational selection (i.e. selection to match host codon preferences) doesn’t seem to do much in RNA viruses. 翻译选择（Translational selection。即选择与宿主密码子偏好匹配）对RNA病毒似乎没有什么影响。
They ignored host-specific constraints based on immune response, specifically how mammals use CpG dinucleotides to recognize foreign DNA/RNA and trigger an immune response. In doing so, they categorized changes in codon bias as deleterious when they were almost certainly adaptive. 他们忽略了基于免疫反应的宿主特定限制，特别是哺乳动物如何使用CpG二核苷酸来识别外来DNA/RNA并触发免疫反应的机制。于是，他们把密码子偏向的变化归为有害的，实际上这些变化几乎肯定是适应性的。
They conflated virulence (how sick a virus makes you) with fitness (viral reproductive success). Not the same thing. And sometimes inversely correlated. 他们把毒性（virulence，病毒使你生病的程度）和适应性（病毒的繁殖成功率）混为一谈。这不是一回事。而且有时还成反比关系。
Related, in using virulence as a proxy for fitness, they ignored the major advances in medicine from 1918 to the 2000s, including the introduction of antibiotics, which is kind of a big deal, since back then and still today, most serious influenza cases and deaths are due to secondary pneumonia infections.
So no, we’ve never documented an instance of error catastrophe. Not in the lab. Not in H1N1.
与此关联的是，在使用毒性作为适应性的替代时，他们忽略了从1918年到2000年代医学上的重大进步，包括抗生素的引入。这一点非常重要，因为无论当时还是今天，大多数严重的流感病例和死亡都是由继发性肺炎感染引起的。
所以，我们从来没有记录到任何错误灾难的实例。在实验室里没有。在H1N1中也没有。
P3: “Genetic entropy” supposedly works like this: Mutations that are only a little bit harmful (dubbed “very slightly deleterious mutations” or VSDMs) occur, and because they are only a teensy bit bad, they cannot be selected out of the population. So they accumulate, and at some point, they build up to the point where they are harmful, and at that point it’s too late; everybody is burdened by the harmful mutations, has low fitness, and the population ultimately goes extinct.
P3：”基因熵 “的工作机制据说是这样的。只有一点点害处的突变（被称为 “非常轻微的有害突变 “或VSDMs）出现了，由于它们只有一点点害处，所以无法从群体中选择出来。因此，它们可以不断积累，到了某一时刻，积累到有害的地步，到那时一切都晚了；每个人都被有害的突变所累，适应性低下，人类最终灭绝了。
Here are all of the options for how this doesn’t work.
One, you could have a bunch of neutral mutations. Neutral because they have no effect on reproductive output. That’s what neutral means. They accumulate, but there are no fitness effects. So the population doesn’t go extinct – no error catastrophe.
以下是这种事情不会发生的几个理由。
第一，你可以有一堆中性突变。中性的意思是对繁殖力没有影响。它们可以积累起来，但没有适应性效果。所以种群不会灭绝——不存在“错误灾难”。
Or you could have a bunch of harmful mutations. Individually, each with have a small effect on fitness. Individuals who by chance have these mutations have lower fitness, meaning these mutations experience negative selection. Maybe they are selected out of the population. Maybe they persist at low frequency. Either way, the population doesn’t go extinct, since there are always more fit individuals (who don’t have any of the bad mutations) present to outcompete those who do. So no error catastrophe.
或者你可以有一堆有害的突变。单独来看，每个突变对适应性的影响都很小。偶然，拥有这些突变的个体具有较低的适应性，意味着这些突变经历了负选择。也许它们得以从群体中选择出来。也许它们以低频持续存在。无论哪种方式，种群都不会灭绝，因为总有适应性更高的个体（没有任何不良突变的个体）存在，与这些不适合的个体竞争。所以不会有“错误灾难”。
Or, option three, everyone experiences a bunch of mutations all at once. All in one generation, every member of a population gets slammed with a bunch of harmful mutations, and fitness declines precipitously. The average reproductive output falls below 1, and the population goes extinct. This is also not error catastrophe. Error catastrophe requires mutations to accumulate over generations. This all happened in a single generation. It’s lethal mutagenesis, a broader process in which a bunch of mutations cause death or extinction, but it isn’t the more specific error catastrophe.
或者，选项三，每个人都一次性经历了一堆突变。在一代人的时间里，种群的每个成员都被一堆有害的突变所冲击，适应度急剧下降。平均繁殖力下降到1以下，种群就会灭绝。这也不是“错误灾难”。“错误灾难”需要突变经过几代的积累。这一切都发生在一代之中。这是致命的诱变，一个更广泛的过程——一堆突变导致了死亡或灭绝，但它不是更具体的“错误灾难”。
But we can do a better job making the creationist case for them. Here’s the strongest version of this argument that creationists can make. It’s not that the mutations are neutral, having no fitness effect, and then at some threshold become harmful, and now cause a fitness decline population-wide. It’s that they are neutral alone, but together, they experience epistasis, which just means that two or more mutations interact to have an effect that is different from any of them alone.
但我们还可以做得更好，提出更有利于创造论者的立论。这里是创造论者可以提出的最有力的版本。“基因熵”并不是说突变是中性的，没有适应效果，然后突然在某个选择门槛上变得有害，导致整个群体的适应性下降。正确的描述是，这些突变单独看是中性的，但一起作用于外显，意味着两个或更多的突变相互作用，产生了不同于它们中任何一个所单独产生的影响。
So you can’t select out individual mutations (since they’re neutral), which accumulate in every member of the population over many generations. But subsequent mutations interact (that’s the epistasis), reducing fitness across the board. 因此，你不能选择出个别的突变（因为它们是中性的），只能任凭这些突变在种群的每个成员中经过许多代积累。但随后的突变会相互影响（这就是外显性），全面降低适应性。
But that still doesn’t work. It just pushed back the threshold for when selection happens. Instead of having some optimal baseline that can tolerate a bunch of mutations, we have a much more fragile baseline, wherein any one of a number of mutations causes a fitness decline. 但这仍然不起作用。它只是推后了选择发生的选择门槛。我们不再有一些可以容忍大量突变的最优基线，而是有一个更脆弱的基线，其中任何一个突变都会导致适应度下降。
But as soon as that happens in an individual, those mutations are selected against (because they hurt fitness due to the epistatic effects). So like above, you’d need everyone to get hit all in a single generation. And a one-generation fitness decline isn’t error catastrophe.
So even the best version of this argument fails.
但是，一旦个体发生这种情况，这些突变就会被选择所排除（因为它们会因外显效应而损害健康）。所以就像上面所说的，你需要每个人都在一代中受到打击。而一代人的体能下降并不是错误的灾难。
因此，即使是这个论点的最佳版本也是失败的。
P4 and P5: I’m going to cover these together, since they’re pretty similar and generally work the same way.
P4和P5：我打算把它们放在一起讨论，因为它们非常相似，而且工作机制也类似。
Basically, when you have bunch of mutations, two things operate that make error catastrophe less likely than you would expect.
基本上，当你有一堆突变时，有两件事会使错误灾难的可能性比你预期的要小。
First, the distribution of fitness effects changes as mutations occur. When a deleterious mutation occurs, at least one deleterious mutation (the one that just occurred) is removed from the universe of possible deleterious mutations, and at least one beneficial mutation is added (the back mutation). But there are also additional beneficial mutations that may be possible now, but weren’t before, due to epistasis with that new harmful mutation. These can recover the fitness cost of that mutation, or even work together with it to recover fitness above the initial baseline. These types of mutations are called compensatory mutations, and while Sanford discusses epistasis causing harmful mutations to stack, he does not adequately weigh the effects in the other direction, as I’ve described here.
首先，适应效果的分布随着突变的发生而改变。当一个有害的突变发生时，至少有一个有害的突变（刚刚发生的那个）从可能的有害突变空间中排除了，并且至少有一个有益的突变（反向突变）加入其中。与此同时，因为与新的有害突变联合作用于外显，从前的中性突变如今可能变成额外的有益突变。这些突变可以恢复有害突变带来的适应成本，甚至与该突变一起工作，获得高于初始基线的适应性。这些类型的突变被称为补偿性突变，虽然桑福德讨论了导致有害突变堆积的外显性，但他并没有充分权衡另一个方向的影响，正如我在这里所描述的。
Related, when you have a ton of mutations, you’re just more likely to find the good ones. We actually have evidence that a number of organisms have been selected to maintain higher-than-expected mutations rates, probably due to the advantage this provides. My favorite example is a ssDNA bacteriophage called phiX174. It infects E. coli, but lacks the “check me” sequences that its host uses to correct errors in its own genome. By artificially inserting those sequences into the phage genome, its mutation rate can be substantially decreased. Available evidence says that selection maintains the higher mutation rate. We also see that during mutagenic treatment, viruses can actually become more fit, contrary to expectations.
与此相关，当你有许多的突变时，更有可能找到好的突变。实际上有证据表明，一些生物体刻意选择维持高于预期的突变率，可能是因为高突变提供了优势。我最喜欢的例子是一种叫做phiX174的ssDNA噬菌体。它感染大肠杆菌，但缺乏其宿主用来纠正自身基因组错误的 “自检”序列。通过人为地将这些序列插入噬菌体的基因组中，它的突变率可以大大降低。现有证据表明，这种噬菌体有意选择维持了较高的突变率。我们还看到，与预期相反，在诱变处理过程中，病毒实际上可以变得更适应。
So as mutations occur, beneficial mutations become more likely, and more beneficial mutations will be found. Both processes undercut the notion of “genetic entropy”. 因此，随着突变的发生，有益的突变变得更有可能，更多的有益突变将被发现。这两个过程都削弱了 “基因熵 “的概念。
P6: John Sanford is a liar. There’s really isn’t a diplomatic way to say it. He’s a dishonest hack who misrepresents ideas and data. I’ve covered this before, but I’ll do it again here, for completeness.
P6：约翰-桑福德是个骗子。真的没有什么外交辞令可以说。他是一个不诚实的雇佣军，歪曲观点和数据。我以前讲过这个，但为了完整起见，我在这里再讲一次。
I’m only going to cover one particularly egregious example here, but see here for another I’m going to stick to the use of a distribution of mutation fitness effects from Motoo Kimura’s work, which Sanford modifies in “Genetic Entropy,” and uses to argue that beneficial mutations are too rare to undo the inevitable buildup of harmful mutations. 我在这里只讲一个特别恶劣的例子，另一个例子请看这里。我这里只讨论Motoo Kimura给出的突变适应效应分布。桑福德在《基因熵》一书中对其进行了修改，以支持有益突变太少，无法消除有害突变造成不可避免累积效应的说法。
Now first, Sanford claims to show a “corrected” distribution, since Kimura omitted beneficial mutations entirely from his. Except this “corrected” distribution is based on nothing. No data. No experiments. Nothing. It’s literally “I think this looks about right”. Ta-da! “Corrected”. Sure.
首先，桑福德声称自己展示了一个 “修正的”分布，因为木村在他的分布中完全省略了有益突变。只是这个”修正的”分布没有任何依据。没有数据。没有实验。什么都没有。这简直就是 “我认为这看起来差不多”。Ta-da! “归正了”……当然。
Second, Sanford justifies his distribution by claiming that Kimura omitted beneficial mutations because he knew they are so rare they don’t really matter anyway. He wrote:
第二，为了说明自己的分布有道理，桑福德声称木村省略了有益的突变，因为木村知道这些突变非常罕见，与大局无关。这是他的说法：
In Kimura’s figure, he does not show any mutations to the right of zero – i.e. there are zero beneficial mutations shown. He obviously considered beneficial mutations so rare as to be outside of consideration.
在木村的图中，他没有显示零点右边的任何突变–也就是说，给出的有益突变为零。显然，他认为有益突变非常罕见，不在考虑之列。
Kimura’s rationale was the exact opposite of this. His distribution represents the parameters for a model demonstrating genetic drift (random changes in allele frequency). He wrote:
木村的理由与此完全相反。他的分布代表了一个能够证明遗传漂移（等位基因频率的随机变化）的参数模型。他的原话是：
The situation becomes quite different if slightly advantageous mutations occur at a constant rate independent of environmental conditions. In this case, the evolutionary rate can become enormously higher in a species with a very large population size than in a species with a small population size, contrary to the observed pattern of evolution at the molecular level.
如果稍微有利的突变以独立于环境条件的恒定速率发生，情况将会变得相当不同。在这种情况下，种群规模非常大的物种演化率会比小种群高得多——与我们在分子水平上观察到的演化模式相反。
In other words, if you include beneficial mutations, they are selected for and take over the simulation, completely obscuring the role genetic drift plays. So because they occur too frequently and have too great an effect, they were omitted from consideration. 换句话说，如果把有益突变考虑在内，它们会被高频选择，破坏实验条件，完全掩盖遗传漂移所起的作用。因此，由于它们出现的频率太高，影响太大，所以只能省略，不予考虑。
Okay, let’s give Sanford the benefit of the doubt on the first go. Maybe, despite writing a book that leans heavily on Kimura’s work, and using one of Kimura’s figures, Sanford never actually read Kimura’s work, and honestly didn’t realize hat Kimura’s rationale was the exact opposite of what Sanford claims. Seems improbable, but let’s say it was an honest mistake.
好吧，这一次姑且认为桑福德是无心犯错。也许，尽管桑福德写了一本严重依赖木村工作的书，并使用了木村的某个图，但从未认真读过木村的工作。实际上，他没有意识到木村的理由与他自己的想法完全相反。尽管这似乎不太可能，但我们姑且认为这是一个诚实的错误吧。
The above passage (and the broader context) were specifically pointed out to Sanford, but he persisted in his claim that he was accurately representing Kimura’s work. He wrote:
我们特别向桑福德指出上述段落（以及更广泛的上下文），但他坚持自己的说法，认为自己准确地代表木村的工作。他回应说：
Kimura himself, were he alive, would gladly attest to the fact that beneficial mutations are the rarest type。 木村本人如果还活着，会很高兴地承认有益突变是罕见的。
The interesting thing with that line is that it’s a slight hedge compared to the earlier statement. This indicates two things. First, that Sanford knows he’s wrong about Kimura’s rationale, and second, that he wants to continue to portray Kimura as agreeing with him, even though he clearly knows better.
这句话的有趣之处在于，它与桑福德先前的陈述有着轻微的矛盾。这表明了两件事。第一，桑福德知道他对木村的理解是错误的，第二，尽管他明知道这一点，却想继续拉木村来为自己的观点背书。
There’s more in the link at the top of this section, but this is sufficient to establish that Sanford is a liar. 本节顶部的链接中还有更多内容，但我们这点讨论已经足以证明桑福德是个骗子。
So that’s…I won’t say everything, because this is a deep well, but that’s a reasonable rundown of why nobody should take “genetic entropy” seriously. 所以这就是……好吧，我不说了，因为水太深了——这就是为什么你不应该认真对待 “基因熵”的简单理由。
Creationists, if you want to beat the genetic entropy drum, you need to deal with each one of these points. (Okay maybe not P6, unless you want to defend Sanford.) So if and when you respond, specifically state which point you dispute and why. Be specific. Cite evidence.
创造论者，如果你想利用基因熵作为证据，需要解释上面各个要点。(好吧，P6例外，除非你还想为桑福德辩护。)
如果你打算回应，请具体说明你对哪一点有争议，为什么。请具体。请引用证据。