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Record W2133566518 · doi:10.1139/f05-010

Intergenerational valuation of fisheries resources can justify long-term conservation: a case study in Atlantic cod (<i>Gadus morhua</i>)

2005· article· en· W2133566518 on OpenAlexfundvenueno aff
Cameron H. Ainsworth, U. Rashid Sumaila

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsnot available
FundersUniversity of British Columbia
KeywordsGadusOverfishingDiscountingEconomicsFishingValuation (finance)FisheryBioeconomicsNatural resource economicsStock (firearms)Atlantic codPopulationGeographyBiology

Abstract

fetched live from OpenAlex

Where the conventional model of discounting advocates aggressive harvest policies, intergenerational discounting could have been used to render the historic gross overfishing of Atlantic cod (Gadus morhua) economically unappealing compared with a more conservative long-term strategy. Under these discounting approaches, we compare the historic harvest trend from 1985 (and projected postcollapse earnings) with theoretical optimal harvest profiles determined by an ecosystem model. The optimal scenarios generate less initial harvest than the historic profile but maintain the resource and provide greater yields over the long term. At a discount rate equal to market interest, we demonstrate that it was more economic under conventional valuation to harvest the cod stock to collapse than it would have been to sustain the population. However, under intergenerational valuation, the sustainable optimal scenarios outperform the actual harvest profile. Application of conventional discounting by fishing consortiums may be partly to blame for depletion, yet management fell short of even that ideal.

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 score0.249
Threshold uncertainty score0.987

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 head0.117
GPT teacher head0.237
Teacher spread0.121 · 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

Citations27
Published2005
Admission routes2
Has abstractyes

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