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...

You might also read

Related Articles

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

Sort by

Same author

A portable multiplexed electrochemical biosensor for lactate and pH monitoring in calf saliva.

Biosensors & bioelectronics·2026

Same author

Correction: Hyper-induction of IL-6 after TLR1/2 stimulation in calves with bovine respiratory disease.

PloS one·2025

Same author

Effects of feeding a simulated waste milk on growth, health, fecal microbiota, and antibiotic resistance in dairy heifer calves.

Journal of dairy science·2025

Same author

The effects of offering adequate-quality or high-quality colostrum on the passive immunity, health, growth, and fecal microbiome development of dairy heifer calves.

Journal of dairy science·2025

Same author

In situ pH-Controlled electrochemical sensors for glucose and pH detection in calf saliva.

Biosensors & bioelectronics·2025

Same author

Hyper-induction of IL-6 after TLR1/2 stimulation in calves with bovine respiratory disease.

PloS one·2024

Same journal

AMCD: A multi-domain agricultural crop and flower image dataset for deep learning-based classification.

Data in brief·2026

Same journal

PacBio HiFi sequencing datasets of culture-enriched airborne microbial cave communities from dolomitic Sudwala Caves, South Africa.

Data in brief·2026

Same journal

Element concentration, stable and radioactive isotope data from multiple environmental matrices in two mid-sized Central-European river basins.

Data in brief·2026

Same journal

Cave plan repository: A standardized dataset for morphometric and geological analyses in speleology.

Data in brief·2026

Same journal

Dataset and tools for benchmarking multi-sensor multi-people tracking for ground robots.

Data in brief·2026

Same journal

Data sets on the interaction of FKBP35 from <i>Plasmodium knowlesi</i> and Hsp90 C-terminal pentapeptide.

Data in brief·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 2, 2026

Instrumentation of Near-term Fetal Sheep for Multivariate Chronic Non-anesthetized Recordings

Instrumentation of Near-term Fetal Sheep for Multivariate Chronic Non-anesthetized Recordings

Published on: October 25, 2015

ActBeCalf: Accelerometer-based multivariate time-series dataset for calf behavior classification.

Oshana Iddi Dissanayake^1,2, Sarah E McPherson^2,3,4, Joseph Allyndrée^5,2

¹School of Computer Science, University College Dublin, Ireland.

|April 15, 2025

Summary

This summary is machine-generated.

This study introduces ActBeCalf, a new dataset for classifying pre-weaned calf behavior using accelerometer data. It enables accurate machine learning models to improve calf welfare on dairy farms.

Keywords:

Accelerometer data Calf behavior classification Machine learning Multivariate time-series

More Related Videos

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

Related Experiment Videos

Last Updated: May 2, 2026

Instrumentation of Near-term Fetal Sheep for Multivariate Chronic Non-anesthetized Recordings

Instrumentation of Near-term Fetal Sheep for Multivariate Chronic Non-anesthetized Recordings

Published on: October 25, 2015

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

Area of Science:

Animal Science
Machine Learning
Data Science

Background:

Improving pre-weaned calf welfare is crucial for dairy farms.
Automatic behavior monitoring using accelerometers requires accurately labeled data.
Time synchronization and data alignment are significant challenges in this field.

Purpose of the Study:

To introduce ActBeCalf, a novel dataset for calf behavior classification.
To address the challenge of aligning accelerometer time-series data with behavioral labels.
To provide a reliable resource for developing machine learning models for calf behavior analysis.

Main Methods:

Thirty pre-weaned calves were equipped with 3D-accelerometer sensors for 13 weeks.
Calf behaviors were manually annotated from video recordings using BORIS software.
Accelerometer data was synchronized and aligned with behavioral annotations using an external clock.

Main Results:

ActBeCalf dataset contains 27.4 hours of aligned accelerometer data from 30 calves.
Behaviors annotated include lying, standing, walking, running, sniffing, scratching, social interaction, and grooming.
Machine learning models developed using ActBeCalf achieved high predictive performance (balanced accuracy: 92% and 84%).

Conclusions:

ActBeCalf is a reliable and comprehensive dataset for research on pre-weaned calf behavior.
The dataset facilitates the development of advanced machine learning models for animal behavior classification.
ActBeCalf supports initiatives aimed at enhancing animal welfare in dairy farming.