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When reproductive value exceeds economic value: an example from the Newfoundland cod fishery

2012· article· en· W1502049575 on OpenAlexafffundabout
Cailin Xu, David C. Schneider, Cassandra Rideout

Bibliographic record

VenueFish and Fisheries · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaEli Lilly and Company
KeywordsGadusFisheryReproductive valueFishingValue (mathematics)PopulationSustainabilityFish stockStock (firearms)Liberian dollarEconomicsNatural resource economicsGeographyEcologyBiologyFish <Actinopterygii>DemographyMathematicsFinance

Abstract

fetched live from OpenAlex

Abstract The Northern Cod ( Gadus morhua ) fishery supported the removal of approximately 200 000 tonnes per year for centuries until collapse in the late 1980s and closure in 1992. Long recovery times, on the order of a decade or more, are now known to be regular concomitant of steep population declines in fish populations. We investigated reproductive value as an alternative to economic value in assessing fisheries sustainability. Our analysis showed that in cod, price‐driven heavy fishing (i.e. fishing mortality positively related to body mass and its dollar value) dramatically impaired reproductive capacity by sacrificing future egg production of large fish. Management based upon current value (either in terms of biomass or equivalently dollar value) substantially underestimates the value of large individuals to the stock; this drove the Northern Cod stock towards collapse by failing to protect individuals with high future value. Our results provide a general explanation for the erosion, collapse and prolonged recovery of a long‐lived species where reproductive value increases rapidly with increasing size. Failure to compute and communicate future value relative to current dollar value to resource harvesters leads to unrealistic perceptions of sustainable resource use.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score0.986

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 communication0.0000.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.000

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 head0.034
GPT teacher head0.234
Teacher spread0.200 · 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

Citations14
Published2012
Admission routes3
Has abstractyes

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