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Record W2101194790 · doi:10.1577/m03-134.1

Spatial Considerations in the Management of Atlantic Cod off Nova Scotia, Canada

2004· article· en· W2101194790 on OpenAlexaffabout
Caihong Fu, L. Paul Fanning

Bibliographic record

VenueNorth American Journal of Fisheries Management · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
Fundersnot available
KeywordsGadusOverfishingNova scotiaStock (firearms)GeographyFisheryFishingFisheries managementStock assessmentPopulationOverexploitationAtlantic codPopulation modelGadidaeBiologyDemographyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Abstract A spatial, age-structured population dynamics model was developed for a stock of Atlantic cod Gadus morhua off eastern Nova Scotia to examine alternative management options. The model incorporates stock structure, seasonal migration, predation, Allee effects, and variation in natural mortality. The simulated dynamics of two substocks were compared under three management options: (1) historical catch levels; (2) combined management (substocks are managed as one unit) with a constant fishing mortality rate (F) of 0.4; and (3) separated management (substocks are managed individually) with the same F level. Three major conclusions emerged from the simulations. First, the population dynamics under historical catch levels tended to vary widely from complete extinction to a fourfold increase. Second, constant-F management produced stable population dynamics, reduced the probability of stock decline, and yielded a higher average catch. Third, the combined management option resulted in overfishing the more vulnerable substock; separated management helped to prevent the more vulnerable substock from collapse, but its performance was compromised when there was net immigration to the more vulnerable substock.

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)0.000
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 score0.394
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.217
Teacher spread0.206 · 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

Citations59
Published2004
Admission routes2
Has abstractyes

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