Jove
Visualize
联系我们

相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

52
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
52
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Psychology as a Science01:13

Psychology as a Science

694
Psychology, as a scientific discipline, aims to understand the mind and behavior through rigorous and systematic methods. The foundation of psychological research is evidence-based, relying heavily on the scientific method to derive and validate knowledge. This structured approach ensures that findings are reliable, valid, and applicable to broader contexts.
The scientific method in psychology involves six critical steps: making observations, formulating hypotheses, conducting tests, analyzing...
694

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

A reporting checklist for large language models in behavioural science.

Nature human behaviour·2026
Same author

State media control influences large language models.

Nature·2026
Same author

Investigating the analytical robustness of the social and behavioural sciences.

Nature·2026
Same author

Individual Brain Charting: fifth release of high-resolution fMRI data for cognitive mapping.

Scientific data·2026
Same author

Assessing the Impact of Sociodemographic Factors on Artificial Intelligence Models in Predicting Dementia: Retrospective Cohort Study.

JMIR medical informatics·2026
Same author

Ethical dilemmas in the use of artificial intelligence in transfusion medicine.

Vox sanguinis·2026
Same journal

Dynamic neutrophil lipidome remodeling during induction of NETosis.

Science advances·2026
Same journal

FUBL-3/FUBP1 mediates mitochondrial stress-induced chromatin remodeling and longevity.

Science advances·2026
Same journal

Distinct projections of PVN oxytocin neurons regulate the divergent defensive behaviors in response to social threat.

Science advances·2026
Same journal

Realization of room-temperature magnetism and multistep magnetization switching in 2D metallic ferrimagnets.

Science advances·2026
Same journal

A battery-free wireless intelligent aligner for spatially resolved, closed-loop theranostics of chronic oral diseases.

Science advances·2026
Same journal

Clearing the noise to predict the rhythm of the North Atlantic climate.

Science advances·2026
查看所有相关文章
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策
  1. 首页
  3. 改革:基于机器学习的科学共识的建议.
  1. 首页
  3. 改革:基于机器学习的科学共识的建议.

相关实验视频

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.5K

改革:基于机器学习的科学共识的建议.

Sayash Kapoor1,2, Emily M Cantrell3,4, Kenny Peng5

  • 1Department of Computer Science, Princeton University, Princeton, NJ 08544, USA.

Science advances
|May 1, 2024

在PubMed 上查看摘要

概括
此摘要是机器生成的。

机器学习 (ML) 的有效性和可重现性的失败在科学中很常见. REFORMS检查清单为基于ML的科学进行和报告提供了明确的指导方针,以提高严谨性和可信度.

更多相关视频

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

548
Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
12:55

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

8.4K

相关实验视频

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.5K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

548
Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties
12:55

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

8.4K

科学领域:

  • 机器学习在科学研究中的跨学科应用.

背景情况:

  • 机器学习 (ML) 方法在科学中的广泛采用受到有效性,可重现性和可概括性的频繁失败的阻碍.
  • 这些失败阻碍了科学进步,促进了对错误发现的共识,并损害了基于机器学习的研究的可信度.
  • 在各种科学学科中观察到ML应用失败的一致模式.

研究的目的:

  • 为科学研究中机器学习的严格应用和透明报告提供可操作的建议.
  • 解决各种领域基于ML的科学研究中遇到的常见陷.

主要方法:

  • 基于广泛的文献审查,制定了REFORMS检查清单 (基于机器学习的科学建议).
  • 达成共识的过程涉及计算机科学,数据科学,数学,社会科学和生物医学科学领域的19名研究人员.
  • 检查表包括32个问题和附带的指导方针.

主要成果:

  • REFORMS检查清单提供了一种结构化的方法,以提高基于机器学习的科学研究质量.
  • 这些指南旨在提高ML应用的有效性,可重复性和通用性.
  • 检查清单是通过严格的,跨学科的共识过程来制定的.

结论:

  • REFORMS检查清单是研究人员设计和执行研究的重要资源.
  • 它有助于同行评审者评估基于ML的研究的方法论健全性.
  • 期刊可以利用REFORMS在科学出版物中强制执行更高的透明度和可重复性标准.