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Aggregates Classification

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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.
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Related Experiment Video

Updated: May 11, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Category clustering calculator for free recall.

Olesya Senkova¹, Hajime Otani

¹Department of Psychology, Central Michigan University, USA.

Advances in Cognitive Psychology

|May 30, 2013

Summary

This study introduces a user-friendly calculator for computing category clustering measures from free recall tests. This tool simplifies the analysis of memory recall strategies for researchers.

Area of Science:

Cognitive Psychology
Neuroscience
Behavioral Science

Background:

Free recall is a widely used measure in memory research.
It assesses both the quantity of recalled information and recall strategies.
Category clustering, a common recall strategy, is difficult to quantify.

Purpose of the Study:

To introduce a novel calculator for computing category clustering measures.
To enhance the user-friendliness of analyzing recall strategies.
To make complex memory research measures more accessible.

Main Methods:

Development of a calculator for category clustering.
Implementation on an accessible online platform.
Testing the calculator's efficiency and ease of use.

Keywords:

category clustering free recall

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

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A Real-world What-Where-When Memory Test

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Main Results:

The calculator automates the computation of category clustering.
It provides a more user-friendly approach to data analysis.
The tool is accessible to a broad range of researchers.

Conclusions:

The developed calculator simplifies the assessment of category clustering in free recall.
This innovation aims to facilitate memory research by reducing analytical burdens.
Increased accessibility of this measure may promote further research into memory strategies.