Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Aggregates Classification

Aggregates Classification

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...

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Spark plasma sintered titania-graphene oxide: A toothless keratoprosthesis for end-stage corneal blindness.

Biomaterials·2026

Same author

Graphene oxide-functionalized nanocomposites promote osteogenesis of human mesenchymal stem cells via enhancement of BMP-SMAD1/5 signaling pathway.

Biomaterials·2021

Same author

Incorporating silica-coated graphene in bioceramic nanocomposites to simultaneously enhance mechanical and biological performance.

Journal of biomedical materials research. Part A·2020

Same author

A sintered graphene/titania material as a synthetic keratoprosthesis skirt for end-stage corneal disorders.

Acta biomaterialia·2019

Same author

Reducing debt improves psychological functioning and changes decision-making in the poor.

Proceedings of the National Academy of Sciences of the United States of America·2019

Same author

Enhancement of Thermoelectric Performance in CuSbSe<sub>2</sub> Nanoplate-Based Pellets by Texture Engineering and Carrier Concentration Optimization.

Small (Weinheim an der Bergstrasse, Germany)·2018

Same journal

Ordering matters in shared authorship: a response to Decius and Schilbach.

Scientometrics·2026

Same journal

Divided by discipline? A systematic literature review on the quantification of online sexism and misogyny using a semi-automated approach.

Scientometrics·2025

Same journal

Science diplomacy: A global research field? Findings from a bibliometric analysis of the science diplomacy scholarship of the past twenty years.

Scientometrics·2025

Same journal

Are questionable research practices considered a successful career strategy? A novel implementation of the implicit association test.

Scientometrics·2025

Same journal

The underexplored effects of economic transition on intellectual property rights protection: An economic geography perspective.

Scientometrics·2025

Same journal

Towards multiple ontologies in science mapping. A tribute to Loet Leydesdorff.

Scientometrics·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 11, 2025

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

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

Published on: December 6, 2024

Evaluating human versus machine learning performance in classifying research abstracts.

Yeow Chong Goh¹, Xin Qing Cai¹, Walter Theseira²

¹School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore.

|August 25, 2020

Summary

This summary is machine-generated.

Machine learning (ML) models are more accurate and reliable than human classifiers for categorizing scientific research abstracts. This cost-effective approach aids comparative bibliometric analysis and research harmonization.

Keywords:

Discipline classification Supervised classification Text classification

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Dec 11, 2025

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

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

Published on: December 6, 2024

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Area of Science:

Bibliometrics
Computational Social Science
Information Science

Background:

Accurate classification of scientific research abstracts is crucial for bibliometric analysis and funding allocation.
Current methods often rely on human expertise, which can be subjective and resource-intensive.
Harmonizing research classifications across different agencies and countries presents a significant challenge.

Purpose of the Study:

To compare the classification performance of machine learning (ML) models against human classifiers for scientific abstracts.
To evaluate the accuracy and reliability of ML algorithms in assigning abstracts to predefined discipline groups.
To assess the cost-effectiveness of ML models for large-scale bibliometric tasks.

Main Methods:

Recruited undergraduate and postgraduate students as human classifiers.
Utilized a support vector machine (SVM) ML algorithm for abstract classification.
Compared human and ML performance on European Research Council Starting Grant project abstracts categorized by evaluation panels.
Employed various training and test datasets to assess model robustness.

Main Results:

ML models demonstrated higher average accuracy than human classifiers across different datasets and evaluation panels.
ML classifiers exhibited greater reliability, showing more consistent classifications compared to human classifiers.
While top-tier human classifiers could match ML in specific instances, their selection and training are more complex and costly.
ML models offer a cost-effective and highly accurate solution for abstract classification.

Conclusions:

Machine learning classification models provide a superior alternative to human classifiers for categorizing scientific research abstracts.
ML's accuracy and reliability make it suitable for comparative bibliometric analysis and harmonizing research classifications.
The cost-effectiveness of ML models supports their adoption for large-scale scientific information management.