Comparative analysis of statistical and machine learning approaches for predicting fish length from otoliths

Accurate estimation of fish length from otoliths is essential for stock assessment, bycatch monitoring, and automated length reconstruction workflows. However, the strength and consistency of otolith-somatic scaling can vary across species and environments. This study examined the predictive perform...

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Principais autores:	Christian James C. Morales, Sanny David P. Lumayno, Ricardo P. Babaran
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Frontiers Media S.A. 2025-12-01
coleção:	Frontiers in Marine Science
Assuntos:	data-limited fisheries demersal and pelagic fish generalized additive models (GAMs) K-nearest neighbors otolith morphometry random forest
Acesso em linha:	https://www.frontiersin.org/articles/10.3389/fmars.2025.1711110/full
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