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Record W4213158089 · doi:10.1071/mf21288

A comparison of catch efficiency and bycatch reduction of tuna pole-and-line fisheries using Japan tuna hook (JT-hook) and circle-shaped hook (C-hook)

2022· article· en· W4213158089 on OpenAlexaff
Khanh Q. Nguyen, Binh Van Nguyen, Huyen Trong Phan, Luong Trong Nguyen, Phuong Van To, Hao Van Tran

Bibliographic record

VenueMarine and Freshwater Research · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicTurtle Biology and Conservation
Canadian institutionsOceans Limited (Canada)Fisheries and Oceans Canada
Fundersnot available
KeywordsHookBycatchYellowfin tunaTunaFisheryThunnusFishingBiologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Unwanted bycatch of sea turtles in the tuna fisheries is a global challenge. To evaluate whether the incidental catch of sea turtles could be reduced through changes in fishing gear, this study compared catch rates and bycatch in the tuna pole-and-line with the addition of above-water lights (PL) fisheries using a Japan tuna hook (JT-hook) and a circle-shaped hook (C-hook). There were two phases to this study. First, five PL fishing vessels that used traditional JT-hooks were compared with five PL fishing vessels that used circle-shaped hooks throughout 1 full year of fishing. Results showed that C-hooks significantly reduced bycatch of sea turtle, while negligibly increasing the catch of yellowfin (Thunnus albacares) and bigeye (Thunnus obesus) tuna. Second, we conducted the onboard research to investigate the effect of JT-hook v. C-hook on the catch rates of commercial PL fishery. Results showed that there were higher catch rates of long snouted lancefish (Alepisaurus ferox) and wahoo (Acanthocybium solandri), but lower catches of thresher shark (Alopias spp.) on C-hooks, with no significant differences for other species considered. Our results suggest that the use of C-hooks in the PL fishery is beneficial to protected endangered sea turtle species.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.072
GPT teacher head0.330
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2022
Admission routes1
Has abstractyes

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