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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.
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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.
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Propagation of Uncertainty from Systematic Error01:10

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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

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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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Related Experiment Video

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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Estimating the predictability of questionable open-access journals.

Han Zhuang1, Lizhen Liang2, Daniel E Acuna3

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

Science Advances
|August 27, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can systematically identify questionable journals by analyzing website data. This scalable approach aids research integrity by flagging thousands of suspect publications, complementing expert review.

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Area of Science:

  • Bibliometrics
  • Scholarly Communication
  • Research Integrity

Background:

  • Questionable journals pose a significant threat to global research integrity.
  • Manual vetting of journals is often slow and lacks scalability.
  • Identifying predatory or unreliable academic publishing venues is a growing concern.

Purpose of the Study:

  • To explore the potential of artificial intelligence (AI) for systematically identifying questionable journals.
  • To develop and evaluate an AI-driven method for assessing journal legitimacy.
  • To provide a scalable solution for detecting unreliable academic publishing venues.

Main Methods:

  • Utilized artificial intelligence (AI) to analyze journal website design, content, and publication metadata.
  • Trained and evaluated the AI model against extensive human-annotated datasets.
  • Adjusted decision thresholds to balance comprehensive screening with precise identification.

Main Results:

  • The AI method achieved practical accuracy in identifying questionable journals.
  • Over 1000 suspect journals were flagged at a balanced threshold.
  • These flagged journals publish a substantial number of articles and receive significant citations.

Conclusions:

  • AI offers a powerful, scalable tool for enhancing research integrity checks.
  • Automated triage using AI can effectively identify potentially problematic journals.
  • Integrating AI with expert review is crucial for robust vetting of academic venues.