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2024-12
Volume 10, issue 04
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ISSN: 2274-0422

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Specimen infos
Collection
Collection : Université de Montpellier : Institut des Sciences de l'Evolution, Montpellier, France

Inventory number : 081118C


Information
Sex : female

Age group : Neonate

Age (if applicable) :

Material Type : head

Origin : North Atlantic

Taxonomy
Class : Chondrichthyes

Order : Carcharhiniformes

Family : Scyliorhinidae

Genus : Scyliorhinus

Species :canicula


Description
Head of a 11.2 cm long Scyliorhinus canicula from a North Atlantic population.

Related article
3D models related to the publication: Hide and seek shark teeth in Random Forests: Machine learning applied to Scyliorhinus canicula
Fidji Berio Logo, Yann Bayle Logo, Sylvie Agret, Daniel Baum Logo, Nicolas Goudemand Logo and Mélanie Debiais-Thibaud Logo
Published online: 24/05/2022

Keywords: geometric morphometrics; machine learning; Scyliorhinus canicula; sharks; tooth morphology

https://doi.org/10.18563/journal.m3.164

Cite this article: Fidji Berio, Yann Bayle, Sylvie Agret, Daniel Baum, Nicolas Goudemand and Mélanie Debiais-Thibaud, 2022. 3D models related to the publication: Hide and seek shark teeth in Random Forests: Machine learning applied to Scyliorhinus canicula. MorphoMuseuM 8:164. doi: 10.18563/journal.m3.164

Export citation

  Abstract

    The present dataset contains the 3D models analyzed in Berio, F., Bayle, Y., Baum, D., Goudemand, N., and Debiais-Thibaud, M. 2022. Hide and seek shark teeth in Random Forests: Machine learning applied to Scyliorhinus canicula. It contains the head surfaces of 56 North Atlantic and Mediterranean small-spotted catsharks Scyliorhinus canicula, from which tooth surfaces were further extracted to perform geometric morphometrics and machine learning. 


  See original publication
  M3 article infos

Published in Volume 08, issue 02 (2022)
PDF

3D data

M3#940

Head of a 11.2 cm long Scyliorhinus canicula female from a North Atlantic population.

Type: "3D_surfaces"

doi: 10.18563/m3.sf.940

Data citation: Fidji Berio, Yann Bayle, Sylvie Agret, Daniel Baum, Nicolas Goudemand and Mélanie Debiais-Thibaud, 2022. M3#940. doi: 10.18563/m3.sf.940

  state:published



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