A machine learning approach for protected species bycatch estimation

Long, Christopher A.; Ahrens, Robert N. M.; Jones, T. Todd; Siders, Zachary A.

doi:10.3389/fmars.2024.1331292

i

A machine learning approach for protected species bycatch estimation

2024
By Long, Christopher A. ; Ahrens, Robert N. M. ; Jones, T. Todd ; ...

Details

Journal Title:

Frontiers in Marine Science
Personal Author:

Long, Christopher A. ; Ahrens, Robert N. M. ; Jones, T. Todd ; Siders, Zachary A.
NOAA Program & Office:

NMFS (National Marine Fisheries Service) ; PIFSC (Pacific Islands Fisheries Science Center)
Description:

Monitoring bycatch of protected species is a fisheries management priority. In practice, protected species bycatch is difficult to precisely or accurately estimate with commonly used ratio estimators or parametric, linear model-based methods. Machine-learning algorithms have been proposed as means of overcoming some of the analytical hurdles in estimating protected species bycatch.
Source:

Frontiers in Marine Science, 11
DOI:

https://doi.org/10.3389/fmars.2024.1331292
ISSN:

2296-7745
Format:

pdf
Publisher:

Frontiers Media SA
Document Type:

Journal Article
Funding:

Grant no. NA21NMF4720548
License:

https://creativecommons.org/licenses/by/4.0/
Rights Information:

CC BY
Compliance:

Library
Main Document Checksum:

urn:sha-512:7d8d5cdf202818b8237afe62521f6e22989c25d649c2f99a8ac545807f9af1fa65af32ea8d73e810cf9fdf2b047083ddc6b51142dade2be30ba8f17e595706e8
Download URL:

https://repository.library.noaa.gov/view/noaa/67905/noaa_67905_DS1.pdf
File Type:

[PDF - 3.22 MB ]

ON THIS PAGE

Details

The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.

BACK TO TOP