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Record W2163582893 · doi:10.1139/f09-160

Temporal patterns of target catch and sea turtle bycatch in the US Atlantic pelagic longline fishing fleet

2009· article· en· W2163582893 on OpenAlexvenueno aff
Connie Y. Kot, André M. Boustany, Patrick N. Halpin

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicTurtle Biology and Conservation
Canadian institutionsnot available
FundersSoutheast Fisheries Science CenterNational Oceanic and Atmospheric AdministrationGordon and Betty Moore Foundation
KeywordsBycatchSwordfishFisherySea turtlePelagic zoneYellowfin tunaFishingTurtle (robot)TunaForagingOceanographyEnvironmental scienceGeographyBiologyEcologyFish <Actinopterygii>Geology

Abstract

fetched live from OpenAlex

Sea turtle bycatch in pelagic longline fishing gear is an ongoing threat to the conservation of sea turtle populations. However, these bycatch events do not occur uniformly in space or time. Leatherback ( Dermochelys coriacea ) and loggerhead ( Caretta caretta ) bycatch rates reported in large fishing regions exhibited different degrees of interannual variability. Target catch and sea turtle bycatch in most regions displayed strong periodicity that corresponded to seasons (~365 days) and/or moon phase (~29 days). When trends in catch and bycatch rates were examined by month and moon phase, the significant periods of higher and lower catch and bycatch related to swordfish ( Xiphias gladius ), yellowfin tuna ( Thunnus albacares ), and sea turtle temporal distributions in foraging and spawning/nesting, oceanographic and prey conditions, and foraging behavior. Catch and bycatch rates tended to depend more on a seasonal rather than a lunar time scale, although there is likely an interaction between the two. These findings provide insights to the susceptibility of target catch and bycatch, regional and temporal patterns of fishing effort, and potential guidance for resource management and conservation.

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 score0.081
Threshold uncertainty score0.975

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.210
Teacher spread0.196 · 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

Citations26
Published2009
Admission routes1
Has abstractyes

Explore more

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