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Record W2163670392 · doi:10.1016/j.icesjms.2004.10.006

Provision of catch advice taking account of non-stationarity in productivity of Atlantic salmon (Salmo salar L.) in the Northwest Atlantic

2004· article· en· W2163670392 on OpenAlexaff
Gérald Chaput, Christopher M. Legault, David G. Reddin, François Caron, Peter G. Amiro

Bibliographic record

VenueICES Journal of Marine Science · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsMinistère des Ressources naturelles et des ForêtsFisheries and Oceans Canada
Fundersnot available
KeywordsSalmoFisheryProductivityPopulationEnvironmental scienceAbundance (ecology)Fisheries managementGeographyFish <Actinopterygii>BiologyFishingEconomicsDemography

Abstract

fetched live from OpenAlex

Abstract The paper presents the data, the models, and the approach for the provision of management advice for a high seas mixed stock fishery on Atlantic salmon (Salmo salar L.). The approach incorporates observation errors, model uncertainty, and considers a possible shift in the productivity of Atlantic salmon. The risk analysis framework further incorporates uncertainty in the fishery harvest characteristics and presents the catch advice as probabilities of meeting or exceeding the conservation objectives relative to catch options. There is very strong evidence from the analyses that there has been a phase shift in productivity of Atlantic salmon of North American origin in the Northwest Atlantic. The change in productivity likely resulted from a change in marine survival which occurred in the early 1990s and has persisted to date. When the uncertainties in the input data are considered, the most parsimonious models suggest that there has been a shift in absolute abundance independent of variations in the spawner index contributing to the recruitment. There continues to be a large amount of uncertainty in the measures of abundance and population dynamics of Atlantic salmon. Uncertainty in the understanding of population dynamics does not necessarily equate to uncertainty in management advice. If model results suggest that spawning objectives are unattainable even when harvest rates are zero, then any harvest level will either accelerate the rate of decline if the model prediction is correct or diminish the probability of recovery if the model prediction is wrong.

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)0.004
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 score0.036
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.275
Teacher spread0.262 · 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

Citations51
Published2004
Admission routes1
Has abstractyes

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