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関連する概念動画

Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Probability Laws01:49

Probability Laws

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Overview
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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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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Updated: Sep 10, 2025

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

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疑わしいオープンアクセスのジャーナルの予測可能性の見積もり

Han Zhuang1, Lizhen Liang2, Daniel E Acuna3

  • 1Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, China.

Science advances
|August 27, 2025
PubMed
まとめ

人工知能 (AI) は,ウェブサイトのデータを分析することで,疑わしいジャーナルを体系的に識別できます. このスケーラブルなアプローチは,何千もの疑わしい出版物を標識し,専門家のレビューを補完することで,研究の完全性を促進します.

科学分野:

  • 図書館情報
  • 学術的なコミュニケーション
  • 研究の誠実さ

背景:

  • 疑わしいジャーナルは グローバルな研究の 誠実さに重大な脅威をもたらします
  • 手動でのジャーナルの検証は 遅くてスケーラビリティがないことが多いです
  • 攻撃的な学術出版社や 信頼できない学術出版社を特定することは 懸念が高まっています

研究 の 目的:

  • 疑わしい雑誌を体系的に特定するための人工知能 (AI) の可能性を探求する.
  • 雑誌の正当性を評価するためのAI駆動の方法の開発と評価.
  • 不確実な学術出版場所の検出のためのスケーラブルなソリューションを提供する.

主な方法:

  • 人工知能 (AI) を活用して,雑誌のウェブサイトのデザイン,コンテンツ,および出版メタデータを分析した.
  • 人工知能モデルを訓練し,人間による詳細なデータセットに対して評価した.
  • 精密な識別と包括的なスクリーニングのバランスを取るために調整された意思決定の値.

主要な成果:

  • AIの方法は,疑わしい雑誌を特定する上で実用的な精度を達成しました.
  • 1000以上の疑わしい日記がバランスのとれた値でマークされました.

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  • これらの雑誌は,かなりの数の記事を掲載し,重要な引用を受けています.
  • 結論:

    • AIは 研究の整合性チェックを強化するための 強力で拡張可能なツールを提供します
    • AIを用いた自動トリアージは 潜在的に問題のあるジャーナルを効果的に特定できます
    • 専門家のレビューとAIを統合することは学術施設の強力な審査に不可欠です.