Evidence map›Paper›PMID 37872950›Full record

ArticlePeerJ2023

Fishery catch records support machine learning-based prediction of illegal fishing off US West Coast.

Jordan T Watson, Robert Ames, Brett Holycross, Jenny Suter, Kayleigh Somers, Camille Kohler, Brian Corrigan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jordan T WatsonPacific Islands Ocean Observing System, University of Hawaii at Manoa, Honolulu, HI, United States of America.
Robert AmesPacific States Marine Fisheries Commission, Portland, OR, United States of America.
Brett HolycrossPacific States Marine Fisheries Commission, Portland, OR, United States of America.
Jenny SuterPacific States Marine Fisheries Commission, Portland, OR, United States of America.
Kayleigh SomersNorthwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, WA, United States of America.
Camille KohlerneXus Data Solutions, LLC, Anchorage, AK, United States of America.
Brian CorriganWest Coast Division, Office of Law Enforcement, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, WA, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Illegal, unreported, and unregulated (IUU) fishing is a major problem worldwide, often made more challenging by a lack of at-sea and shoreside monitoring of commercial fishery catches. Off the US West Coast, as in many places, a primary concern for enforcement and management is whether vessels are illegally fishing in locations where they are not permitted to fish. We explored the use of supervised machine learning analysis in a partially observed fishery to identify potentially illicit behaviors when vessels did not have observers on board. We built classification models (random forest and gradient boosting ensemble tree estimators) using labeled data from nearly 10,000 fishing trips for which we had landing records (

Indexed as

Conservation of Natural ResourcesFisheriesAnimalsHumansHuntingMachine LearningResearch PersonnelFisher behaviorFisheries enforcementFishing predictionGradient boostingIllegal, Unreported, Unregulated (IUU) fishingOperationalizationRandom forestsSpecies distribution

Identifiers

PMID37872950
PMCPMC10590572

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.