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相关概念视频

Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Detection of Gross Error: The Q Test01:00

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Quantifying Work02:30

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As a system undergoes a change, its internal energy can change, and energy can be transferred from the system to the surroundings, or from the surroundings to the system. 
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Aggregates Classification01:29

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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相关实验视频

Updated: Jul 23, 2025

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking
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在文本注释任务中,ChatGPT的表现优于众筹工作者.

Fabrizio Gilardi1, Meysam Alizadeh1, Maël Kubli1

  • 1Department of Political Science, University of Zurich, Zurich 8050, Switzerland.

Proceedings of the National Academy of Sciences of the United States of America
|July 18, 2023
PubMed
概括
此摘要是机器生成的。

在文本分类任务 (如相关性和主题检测) 中,ChatGPT显著优于人类注释者. 这种人工智能模型提供了更高的准确性和协议,成本很小,彻底改变了自然语言处理应用程序.

关键词:
聊天GPT 聊天 在GPT 聊天人类注释人类注释大型语言模型.文本作为数据的数据.文字分类 文本分类 文本分类

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科学领域:

  • 自然语言处理 (NLP) 是一种自然语言处理.
  • 人工智能 (AI) 是一种人工智能.
  • 机器学习 机器学习

背景情况:

  • 手动文本注释对于培训NLP分类器和评估模型至关重要.
  • 任务范围从简单的相关性到复杂的检测,通常由人群工作者或训练有素的注释者执行.

研究的目的:

  • 为了比较ChatGPT与人类注释者的性能,用于各种文本注释任务.
  • 评估人工智能驱动注释的准确性,代码间协议和成本效益.

主要方法:

  • 利用了四个推特和新闻文章 (n = 6,183) 的数据集.
  • 评估了ChatGPT在相关性,立场,主题和检测等任务上的零射击性能.
  • 将ChatGPT的结果与群众工作者和训练有素的注释者进行比较.

主要成果:

  • 聊天GPT的零射击精度超过了群众工作者平均25个百分点.
  • 与群众工作者和训练有素的注释者相比,ChatGPT展示了优越的代码间协议.
  • 聊天GPT的每注释成本不到0.003美元,比MTurk便宜得多.

结论:

  • 像ChatGPT这样的大型语言模型显示了提高文本分类效率的巨大潜力.
  • 人工智能驱动的注释为手工方法提供了更准确,更一致,更具成本效益的替代方案.
  • 这一进步可能会彻底改变NLP应用程序的开发和评估方式.