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Record W2792238077 · doi:10.1139/cjfas-2017-0374

American lobster: persistence in the face of high, size-selective, fishing mortality — a perspective from the southern Gulf of St. Lawrence

2018· article· en· W2792238077 on OpenAlexaffvenue
Michel Comeau, J. Mark Hanson

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicCrustacean biology and ecology
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsHomarusAmerican lobsterGadusFisheryFishingPopulationAtlantic codBiologyFisheries managementPopulation sizeEcologyCrustaceanGeographyFish <Actinopterygii>Demography

Abstract

fetched live from OpenAlex

The American lobster (Homarus americanus) population in southern Gulf of St. Lawrence has long been subjected to high exploitation, and yet its population is currently at a high and increasing abundance level. The lobster fishery management is based on effort-control, with a short season, mandatory release of egg-bearing females, and strict enforcement of regulations. Another important factor is the high survival of lobster returned to the water. The combination of a minimum legal size limit and either an upper size limit for females or an effective size limit due to entrance-ring size on the traps has resulted in a slot fishery after which the larger, most fecund animals have low vulnerability to the fishery. These efforts to protect large individuals have had a positive effect on lobster larval production, which may lead to even higher adult population numbers. Comparisons with the management of snow crab (Chionoecetes opilio) and Atlantic cod (Gadus morhua) quota-based fisheries were made to try to explain the different trajectories that these three species’ populations have taken since the 1960s.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.709
Threshold uncertainty score0.579

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.024
GPT teacher head0.232
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 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

Citations9
Published2018
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicCrustacean biology and ecologyFrench-language works237,207