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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Autors principals:	Christian James C. Morales, Sanny David P. Lumayno, Ricardo P. Babaran
Format:	Artigo
Idioma:	Inglês
Publicat:	Frontiers Media S.A. 2025-12-01
Col·lecció:	Frontiers in Marine Science
Matèries:	data-limited fisheries demersal and pelagic fish generalized additive models (GAMs) K-nearest neighbors otolith morphometry random forest
Accés en línia:	https://www.frontiersin.org/articles/10.3389/fmars.2025.1711110/full
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