Machine learning based assessment of small-bodied fish tracking to evaluate spoiler baffle fish passage design.

Magaju, Dipendra; Montgomery, John; Franklin, Paul; Baker, Cindy; Friedrich, Heide

Magaju, Dipendra; Montgomery, John; Franklin, Paul; Baker, Cindy; Friedrich, Heide.

Afiliación

Magaju D; Department of Civil and Environmental Engineering, University of Auckland, Auckland, New Zealand. Electronic address: dmag588@aucklanduni.ac.nz.
Montgomery J; Institute of Marine Science, University of Auckland, Auckland, New Zealand.
Franklin P; National Institute of Water and Atmospheric Research, Hamilton, New Zealand.
Baker C; National Institute of Water and Atmospheric Research, Hamilton, New Zealand.
Friedrich H; Department of Civil and Environmental Engineering, University of Auckland, Auckland, New Zealand.

J Environ Manage ; 325(Pt A): 116507, 2023 Jan 01.

Article en En | MEDLINE | ID: mdl-36270125

Asunto(s)

Osmeriformes; Animales; Ecosistema; Ríos; Aprendizaje Automático

Palabras clave

DeepLabCut; Fish behaviour; Fish kinematics; Galaxias maculatus; Machine learning; Spoiler baffles

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Osmeriformes Límite: Animals Idioma: En Revista: J Environ Manage Año: 2023 Tipo del documento: Article