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Record W3198645273 · doi:10.1093/icesjms/fsab153

Five centuries of cod catches in Eastern Canada

2021· article· en· W3198645273 on OpenAlexafffundabout
Rebecca Schijns, Rainer Froese, Jeffrey A. Hutchings, Daniel Pauly

Bibliographic record

VenueICES Journal of Marine Science · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsDalhousie UniversityUniversity of British Columbia
FundersBundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und VerbraucherschutzNatural Sciences and Engineering Research Council of CanadaKillam Trusts
KeywordsGadusFishingFisheryStock (firearms)GeographyPopulationBiologyDemographyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Abstract The fishery for Northern Atlantic cod (Gadus morhua) off Newfoundland and Labrador, Eastern Canada, presents the most spectacular case of an exploited stock crashed in a few decades by an industrial bottom trawl fishery under a seemingly sophisticated management regime after half a millennium of sustainable fishing. The fishery, which had generated annual catches of 100000 to 200000 tonnes from the beginning of the 16th century to the 1950s, peaked in 1968 at 810000 tonnes, followed by a devastating collapse and closure 24 years later. Since then, stock recovery may have been hindered by premature openings, with vessels targeting the remains of the cod population. Previous research paid little attention towards using multicentury time series to inform sustainable catches and recovery plans. Here, we show that a simple stock assessment model can be used to model the cod population trajectory for the entire period from 1508 to 2019 for which catch estimates are available. The model suggests that if fishing effort and mortality had been stabilized in the 1980s, precautionary annual yields of about 200000 tonnes could have been sustained. Our analysis demonstrates the value of incorporating prior knowledge to counteract shifting baseline effects on reference points and contemporary perceptions of historical stock status.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.242
Teacher spread0.231 · 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.

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

Citations49
Published2021
Admission routes3
Has abstractyes

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