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Record W2156395764 · doi:10.1139/cjfas-2013-0561

Mortality rate of silky sharks (<i>Carcharhinus falciformis</i>) caught in the tropical tuna purse seine fishery in the Indian Ocean

2014· article· en· W2156395764 on OpenAlexvenueno aff
François Poisson, John D. Filmalter, Anne-Lise Vernet, Laurent Dagorn

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsnot available
FundersInternational Seafood Sustainability Foundation
KeywordsCarcharhinusFisheryTunaFishingMortality rateThunnusBiologyFish <Actinopterygii>Pacific oceanGeographyDemographyOceanography

Abstract

fetched live from OpenAlex

Scientists aboard French purse seine vessels recorded the number and condition of silky sharks (Carcharhinus falciformis) caught during three fishing cruises in the Indian Ocean. A sample of 31 individuals that showed signs of life were tagged with satellite tags to investigate their postrelease mortality. The majority of individuals (95%) were brought on board using the brailer. Combining the proportion of sharks that were dead (72%) and the mortality rate of those released (48%), the overall mortality rate of brailed individuals was 85%. Few individuals (5%) were not brailed as they were entangled and landed during the hauling process. The survival rate of these individuals was high, with an overall mortality rate of meshed individuals of 18%. The combination of these two categories led to an overall mortality rate of 81%. This high value reflects the harsh conditions encountered by sharks during the purse seine fishing process. Consequently, methods that prevent sharks being brought on board are a priority for future investigations, but good handling practices should also be promoted as they could reduce mortality by at least 19%.

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)0.002
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 score0.330
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.223
Teacher spread0.208 · 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

Citations65
Published2014
Admission routes1
Has abstractyes

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