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Record W1969990044 · doi:10.1139/cjfas-2014-0471

Modelling age-dependent movement: an application to red and gag groupers in the Gulf of Mexico

2015· article· en· W1969990044 on OpenAlexaffvenue
Thomas R. Carruthers, John F. Walter, Murdoch K. McAllister, Meaghan D. Bryan

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of British Columbia
FundersSoutheast Fisheries Science Center
KeywordsGrouperFisheryPopulationMaximum sustainable yieldSpatial distributionSerranidaeSubmarine pipelineGeographyBiologyFisheries managementOceanographyFish <Actinopterygii>FishingGeologyRemote sensingDemography

Abstract

fetched live from OpenAlex

We develop and test spatial population dynamics models that estimate age-dependent offshore movement of fish populations from spatial fishery data. Spatially aggregated population dynamics models produced biased estimates of maximum sustainable yield (MSY) reference points when spatial dynamics were simulated. Spatial population dynamics models provided relatively unbiased estimates of MSY reference points regardless of whether spatial dynamics were simulated. We demonstrate that by using conventional fishery data that are disaggregated spatially, it is possible to estimate movement with age and obtain more accurate estimates of management reference points. The new spatial models were fitted to data for Gulf of Mexico red grouper (Epinephelus morio) and gag grouper (Mycteroperca microlepis) to estimate spatial distribution and offshore movement with age. Offshore ontogeny was estimated to be stronger for gag grouper than red grouper and predicted a larger fraction of older gag grouper in deeper waters.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.489

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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 science0.0010.001
Research integrity0.0010.000
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 head0.042
GPT teacher head0.250
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations29
Published2015
Admission routes2
Has abstractyes

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