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Record W2037541565 · doi:10.2960/j.v39.m586

Fishing in the NAFO Regulatory Area: Integrated Modeling of Resources, Social Impacts in Canada and EU Fleet Viability

2008· article· en· W2037541565 on OpenAlexaffabout
Daniel E. Lane

Bibliographic record

VenueJournal of Northwest Atlantic Fishery Science · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsGroundfishFishingFisheryFisheries managementStock (firearms)Stock assessmentFish stockCommercial fishingBusinessGeography

Abstract

fetched live from OpenAlex

This paper presents an integrated spreadsheet model of the biological, social, and economic attributes of the international groundfishery on the Grand Banks in NAFO Divisions 3LNO. The historical involvement of the fisheries is traced from 1972 to the present. The model explores the impacts of the fishery on: (1) the status of six key Grand Banks groundfish stock populations, (2) the economics of the international commercial fishery, especially as experienced by Canada, Spain and Portugal, and (3) the social status of Atlantic Canadian coastal communities dependent on the Grand Banks fishery. Groundfish catches in the Grand Banks NAFO Regulatory Area (NRA) are used to estimate the implications on the annual integrated (harvesting and processing) economic per-formance of the fishing fleets. Social impacts of the fisheries on the labour opportunities in Canadian communities are also analysed. The integrated spreadsheet model is used to explore the impacts on stocks, fisheries, and communities under alternative assumptions about actual fishery removals. The results enable an improved understanding of the underlying historical behaviour of declining Northwest Atlantic groundfish stocks, economic viability from fishing, and the decline of Canadian coastal communities. Evidence from integrated modeling point out shortcomings associated with not

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 score0.082
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.012
GPT teacher head0.188
Teacher spread0.176 · 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

Citations6
Published2008
Admission routes2
Has abstractyes

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