Edit "better-bayes (chinese)" by @alex-mayee

2020/better-bayes/chinese/sentence_translations.json

@@ -1,257 +1,257 @@
 [
  {
   "input": "Some of you may have heard this paradoxical fact about medical tests.",
-  "translatedText": "你们中的一些人可能听说过这个关于医学检查的矛盾事实。",
+  "translatedText": "你们其中一些人可能听说过关于医学检查的悖论。",
   "model": "google_nmt",
-  "n_reviews": 0,
-  "start": 0.0,
+  "n_reviews": 1,
+  "start": 0,
   "end": 3.14
  },
  {
   "input": "It's very commonly used to introduce the topic of Bayes' rule in probability.",
-  "translatedText": "它非常常用于介绍概率中的贝叶斯规则主题。",
+  "translatedText": "它经常用于介绍概率论中的贝氏定理。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 3.58,
   "end": 6.74
  },
  {
   "input": "The paradox is that you could take a test which is highly accurate, in the sense that it gives correct results to a large majority of the people taking it.",
-  "translatedText": "矛盾的是，您可以进行高度准确的测试，因为它 可以为大多数参加测试的人提供正确的结果。",
+  "translatedText": "悖论的大概是，你可以进行高准确度的测试，一个可以能为大多数参加测试的人提供正确的结果。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 7.5,
   "end": 15.66
  },
  {
   "input": "And yet, under the right circumstances, when assessing the probability that your particular test result is correct, you can still land on a very low number, arbitrarily low, in fact.",
-  "translatedText": "然而，在适当的情况下，当评估您的特定测试结果正确的概率 时，您仍然可以得到一个非常低的数字，实际上是任意低的。",
+  "translatedText": "然而，在某些情况下，当你在评估测试结果是否正确的概率时，你仍然可以得到一个正确率非常低的数字，一个任意的数字。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 16.04,
   "end": 26.3
  },
  {
   "input": "In short, an accurate test is not necessarily a very predictive test.",
-  "translatedText": "简而言之，准确的测试不一定是预测性很强的测试。",
+  "translatedText": "简而言之，一个高准确度的测试不一定是个预测性很强的测试。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 26.78,
   "end": 31.82
  },
  {
   "input": "Now when people think about math and formulas, they don't often think of it as a design process.",
-  "translatedText": "现在，当人们思考数学和公式时，他们通常不会将其视为设计过程。",
+  "translatedText": "一般而言，当人们思考数学和公式时，他们不会将其视为设计过程。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 33.06,
   "end": 37.44
  },
  {
   "input": "I mean, maybe in the case of notation it's easy to see that different choices are possible, but when it comes to the structure of the formulas themselves and how we use them, that's something that people typically view as fixed.",
-  "translatedText": "我的意思是，也许在符号的情况下，很容易看出不同 的选择是可能的，但当涉及到公式本身的结构以及 我们如何使用它们时，人们通常认为这是固定的。",
+  "translatedText": "我是说，也许在运用数学符号的情况下，我們会有选择性，但当涉及到公式本身的结构以及我们如何使用它们时，人们通常认为这是固定的。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 38.08,
   "end": 49.68
  },
  {
   "input": "In this video, you and I will dig into this paradox, but instead of using it to talk about the usual version of Bayes' rule, I'd like to motivate an alternate version, an alternate design choice.",
-  "translatedText": "在这个视频中，你和我将深入研究这个悖论，但 我不想用它来讨论贝叶斯规则的通常版本，而 是想激发一个替代版本，一种替代设计选择。",
+  "translatedText": "在这个视频中，你我将深入研究这个悖论，但我不想用它来研讨通常版的贝氏规则，而是想激发另一个版本，另一种替代的设计选择。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 50.68,
   "end": 60.56
  },
  {
   "input": "Now, what's up on the screen now is a little bit abstract, which makes it difficult to justify that there really is a substantive difference here, especially when I haven't explained either one yet.",
-  "translatedText": "现在，屏幕上显示的内容有点抽象，这使 得很难证明这里确实存在实质性差异，特 别是当我还没有解释其中任何一个时。",
+  "translatedText": "视频上正在显示的内容有点抽象，确实很难证明这里存在实质性的差异，特别是当我还没有作任何的解释。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 61.66,
   "end": 70.54
  },
  {
   "input": "To see what I'm talking about though, we should really start by spending some time a little more concretely, and just laying out what exactly this paradox is.",
-  "translatedText": "不过，为了明白我在说什么，我们真的应该首先 花一些时间更具体地阐述这个悖论到底是什么。",
+  "translatedText": "为了解释这一点，我们首先应该具体地阐述确证悖论到底是什么。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 71.04,
   "end": 78.1
  },
  {
   "input": "Picture a thousand women and suppose that 1% of them have breast cancer.",
-  "translatedText": "1% 的女性患有乳腺癌 想象 1000 名女性，并假设其中 1% 患有乳腺癌。",
+  "translatedText": "假设 1000 名女性中，有 1% 患有乳腺癌。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 84.02,
   "end": 87.94
  },
  {
   "input": "And let's say they all undergo a certain breast cancer screening, and that 9 of those with cancer correctly get positive results, and there's one false negative.",
-  "translatedText": "假设他们都接受了某种乳腺癌筛查，其中 9 名癌症 患者正确获得了阳性结果，还有 1 名假阴性结果。",
+  "translatedText": "假设每位都接受了某种乳腺癌筛查，其中 9 名患者获得了真阳性结果，还有 1 名得了假阴性结果。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 88.68,
   "end": 96.68
  },
  {
   "input": "And then suppose that among the remainder without cancer, 89 get false positives, and 901 correctly get negative results.",
-  "translatedText": "然后假设在其余未患癌症的人中，89 人得到 假阳性结果，901 人正确得到阴性结果。",
+  "translatedText": "在其余未患癌症的女性中， 89 人得到假阳性结果， 901 人得到真阴性结果。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 97.48,
   "end": 104.92
  },
  {
   "input": "So if all you know about a woman is that she does the screening and she gets a positive result, you don't have information about symptoms or anything like that, you know that she's either one of these 9 true positives or one of these 89 false positives.",
-  "translatedText": "因此，如果您对一位女性的了解只是她进行了筛查并且得到了阳性 结果，那么您没有有关症状或类似信息的信息，您就知道她要么是 这 9 种真阳性之一，要么是这 89 种真阳性之一误报。",
+  "translatedText": "因此，如果你对某位女性的了解只有她进行了筛查之后获得到了阳性的结果，并且没有任何相关症状的信息。你就知道她要么就是 9 位真阳性之一，或者是 89 位假阳性之一。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 105.72,
   "end": 118.26
  },
  {
   "input": "So the probability that she's in the cancer group given the test result is 9 divided by 9 plus 89, which is approximately 1 in 11.",
-  "translatedText": "因此，根据测试结果，她属于癌症组的概率为 9 除以 9 加 89，大约为 11 分之一。",
+  "translatedText": "因此，根据筛查结果，她属于癌症组的概率为 9 除以 9 加 89，大约为 11 分之一。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 119.36,
   "end": 128.14
  },
  {
   "input": "In medical parlance, you would call this the positive predictive value of the test, or PPV, the number of true positives divided by the total number of positive test results.",
-  "translatedText": "用医学术语来说，您可以将其称为测试的阳性预测值 或 PPV，即真阳性数量除以阳性测试结果总数。",
+  "translatedText": "以医学术语来说，你可以这称为测试的阳性预测值 或 PPV，即真阳性数量除以阳性结果的总数。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 129.08,
   "end": 138.62
  },
  {
   "input": "You can see where the name comes from.",
-  "translatedText": "你可以看到这个名字的由来。",
+  "translatedText": "你可以借此看到这个名称的由来。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 138.62,
   "end": 140.44
  },
  {
   "input": "To what extent does a positive test result actually predict that you have the disease?",
-  "translatedText": "阳性检测结果在多大程度上实际上可以预测您患有这种疾病？",
+  "translatedText": "阳性测试结果在什么程度上可以实际预测你患有某种疾病。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 140.74,
   "end": 145.36
  },
  {
   "input": "Now, hopefully, as I've presented it this way where we're thinking concretely about a sample population, all of this makes perfect sense.",
-  "translatedText": "现在希望，正如我以这种方式呈现的那样，我们具体 考虑了一个样本群体，所有这些都是完全合理的。",
+  "translatedText": "希望我以这种方式呈现，我们具体考虑了一个特定群体，目前的解释是完全合理的。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 146.82,
   "end": 153.46
  },
  {
   "input": "But where it comes across as counterintuitive is if you just look at the accuracy of the test, present it to people as a statistic, and then ask them to make judgments about their test result.",
-  "translatedText": "但如果你只看测试的准确性，将其作为统计数据呈现给人们 ，然后要求他们对测试结果做出判断，就会显得违反直觉。",
+  "translatedText": "但如果你只看测试的准确性，将其作为统计数据呈现给一般人 ，然后要求他们照测试结果做出判断，那就可能会违反直觉。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 153.96,
   "end": 163.2
  },
  {
   "input": "Test accuracy is not actually one number, but two.",
-  "translatedText": "测试准确度实际上不是一个数字，而是两个数字。",
+  "translatedText": "准确度实际上不是一个数字，而是两个。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 164.02,
   "end": 166.26
  },
  {
   "input": "First, you ask how often is the test correct on those with the disease.",
-  "translatedText": "首先，您会问，对患有这种疾病的人进行测试的正确率是多少？",
+  "translatedText": "首先，你该问，在某种疾病的患者中成功确证概率是多少？",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 166.26,
   "end": 171.12
  },
  {
   "input": "This is known as the test sensitivity, as in how sensitive is it to detecting the presence of the disease.",
-  "translatedText": "这被称为测试敏感性，例如，它对检测疾病的存在有多敏感？",
+  "translatedText": "这被称为测试的灵敏度，即是它对测试疾病的存在有多敏感。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 171.7,
   "end": 177.44
  },
  {
   "input": "In our example, test sensitivity is 9 in 10, or 90%.",
-  "translatedText": "在我们的示例中，测试灵敏度为十分之九，即 90%。",
+  "translatedText": "在我们的例子中，灵敏度为十分之九，即 90%。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 178.26,
   "end": 181.26
  },
  {
   "input": "And another way to say the same fact would be to say the false negative rate is 10%.",
-  "translatedText": "同一事实的另一种说法是假阴性率为 10%。",
+  "translatedText": "另一种说法就是假阴性率为 10%。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 182.02,
   "end": 186.68
  },
  {
   "input": "And then a separate, not necessarily related number is how often it's correct for those without the disease, which is known as the test specificity, as in are positive results caused specifically by the disease, or are there confounding triggers giving false positives.",
-  "translatedText": "然后，一个单独的、不一定相关的数字是对于那些没有疾病的 人来说正确的频率，这被称为测试特异性，例如，阳性结果是 由疾病专门引起的，还是存在产生假阳性的混杂触发因素？",
+  "translatedText": "然后，另一个不一定相关的数字是成功排除那些没有患病的人的概率，即为测试的特异度。也就是说，阳性结果到底是由疾病引起的，还是另有其他因素混杂造成假阳性。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 186.68,
   "end": 202.06
  },
  {
   "input": "In our example, the specificity is about 91%.",
-  "translatedText": "在我们的示例中，特异性约为 91%。",
+  "translatedText": "在我们的例子中，特异性约为 91%。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 203.08,
   "end": 206.58
  },
  {
   "input": "Or another way to say the same fact would be to say the false positive rate is 9%.",
-  "translatedText": "或者，用另一种方式表达同一事实就是假阳性率为 9%。",
+  "translatedText": "换句话说，假阳性率为 9%。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 206.58,
   "end": 211.66
  },
  {
   "input": "So the paradox here is that in one sense, the test is over 90% accurate.",
-  "translatedText": "所以这里的悖论是，从某种意义上说，测试的准确率超过 90%。",
+  "translatedText": "所谓的悖论是，从某种意义上说，测试的准确率超过 90%。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 211.66,
   "end": 216.76
  },
  {
   "input": "It gives correct results to over 90% of the patients who take it.",
-  "translatedText": "它为超过 90% 的服用该药物的患者提供了正确的结果。",
+  "translatedText": "超过 90% 的被测试的患者得到正确的结果。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 217.02,
   "end": 220.66
  },
  {
   "input": "And yet, if you learn that someone gets a positive result without any added information, there's actually only a 1 in 11 chance that that particular result is accurate.",
-  "translatedText": "然而，如果您得知某人在没有任何附加信息的情况下获得了阳 性结果，那么该特定结果准确的可能性实际上只有十分之一。",
+  "translatedText": "然而，如果你在没有任何附加信息的情况下得知某人获得了阳性结果，那么该结果实际准确性只有 11 分之 1。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 220.66,
   "end": 229.6
  },
  {
   "input": "This is a bit of a problem, because of all of the places for math to be counterintuitive, medical tests are one area where it matters a lot.",
-  "translatedText": "这是一个有点问题，因为数学在很多地方都违 反直觉，医学测试是一个非常重要的领域。",
+  "translatedText": "这確实是个问题。因为在很多违反直觉的数学概念中，医学测试是一个事关重大的领域。",
   "model": "google_nmt",
-  "n_reviews": 0,
+  "n_reviews": 1,
   "start": 230.62,
   "end": 237.18
  },
@@ -724,7 +724,7 @@
   "translatedText": "另一方面，91% 的特异性意味着 9% 的非癌症患者会得到假阳性结果。",
   "model": "google_nmt",
   "n_reviews": 0,
-  "start": 569.0,
+  "start": 569,
   "end": 575.76
  },
  {
@@ -861,7 +861,7 @@
   "model": "google_nmt",
   "n_reviews": 0,
   "start": 661.62,
-  "end": 669.0
+  "end": 669
  },
  {
   "input": "If you've ever heard someone talk about the chances of an event being 1 to 1 or 2 to 1, things like that, you already know about odds.",
@@ -1004,7 +1004,7 @@
   "translatedText": "提醒一下，这是由 90% 的敏感性除以 9% 的假阳性率得出的。",
   "model": "google_nmt",
   "n_reviews": 0,
-  "start": 791.0,
+  "start": 791,
   "end": 796.56
  },
  {
@@ -1292,7 +1292,7 @@
   "translatedText": "然后我们再次将该项复制到分母中，然后假阳性的比例来自于未患病的先验概率乘以该情况下检测呈阳性的概率。",
   "model": "google_nmt",
   "n_reviews": 0,
-  "start": 1023.0,
+  "start": 1023,
   "end": 1034.1
  },
  {
@@ -1317,7 +1317,7 @@
   "model": "google_nmt",
   "n_reviews": 0,
   "start": 1049.22,
-  "end": 1057.0
+  "end": 1057
  },
  {
   "input": "While that does make for a really elegant little expression, if you intend to use this for calculations, it's a little disingenuous, because in practice, every single time you do this you need to break down that denominator into two separate parts, breaking down the cases.",
@@ -1445,7 +1445,7 @@
   "model": "google_nmt",
   "n_reviews": 0,
   "start": 1137.24,
-  "end": 1142.0
+  "end": 1142
  },
  {
   "input": "Beyond just making calculations easier, there's something I really like about attaching a number to test accuracy that doesn't even look like a probability.",
@@ -1575,4 +1575,4 @@
   "start": 1248.82,
   "end": 1251.04
  }
-]
+]

2020/better-bayes/chinese/title.json

@@ -1,5 +1,5 @@
 {
  "input": "The medical test paradox, and redesigning Bayes' rule",
- "translatedText": "医学测试悖论和重新设计贝叶斯规则",
- "n_reviews": 0
-}
+ "translatedText": "医疗确证悖论与贝叶斯定理的重新设计。",
+ "n_reviews": 1
+}