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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2021
License: CC BY
Data sources: ZENODO
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Bioclimatic classifications for Colombia

Authors: Richard Rios; Elkin A. Noguera-Urbano; Jairo Espinosa; Jose Manuel Ochoa;

Bioclimatic classifications for Colombia

Abstract

Bioclimatic classifications seek to divide a study region into geographic areas with similar bioclimatic characteristics. In this study we proposed two bioclimatic classifications for Colombia using machine learning techniques. We initially obtained a modified Lang classification using dimensionality reduction and classification techniques. We then integrated the modified Lang and Caldas classifications to derive a second bioclimatic classification for Colombia.

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Keywords

Bioclimatic classification, Caldas classification, Lang classification, logistic regression models, dimensionality reduction, Bioclimatic classification, Caldas classification, Lang classification, logistic regression models, dimensionality reduction

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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