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Record W3089054116 · doi:10.7202/1071606ar

Portrait de la pêche hivernale au Québec : historique, gestion et perspectives

2020· article· fr· W3089054116 on OpenAlexaffvenueabout
Simon Bernatchez, Yves Paradis, Catherine Brisson-Bonenfant, Philippe Brodeur, Daniel Hatin, Marie-France Barrette

Bibliographic record

VenueLe Naturaliste canadien · 2020
Typearticle
Languagefr
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMinistère des Ressources naturelles et des Forêts
Fundersnot available
KeywordsHumanitiesArtGeography

Abstract

fetched live from OpenAlex

Entre les années 1960 et 1980, la pêche récréative hivernale au Québec est graduellement passée d’une pratique marginale à une activité souvent structurée et prenant la forme de villages de pêche offrant parfois un service de location de cabanes. Malgré l’importance de cette activité, la pêche hivernale demeure peu étudiée comparativement à la pêche estivale. Cet article présente un portrait actualisé de la pêche hivernale au Québec. Les principales espèces recherchées par les pêcheurs sportifs en hiver sont le doré jaune, le doré noir, la perchaude, le grand brochet, le poulamon atlantique et l’éperlan arc-en-ciel. Dans les systèmes du fleuve Saint-Laurent, de l’estuaire et du golfe du Saint-Laurent, de leurs tributaires et du lac Champlain (baie Missisquoi), 132 sites de pêche hivernale ont été répertoriés en 2018-2019, totalisant environ 4 700 cabanes de pêche. Les informations disponibles sur la pêche hivernale dans certaines régions du Québec, particulièrement en eaux intérieures, demeurent toutefois incomplètes. La pêche hivernale présente de grandes possibilités de mise en valeur, mais elle engendre une pression de pêche qu’il importe de considérer dans la gestion des stocks de poissons au Québec.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.253

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0070.004
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.000

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 head0.009
GPT teacher head0.215
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 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

Citations1
Published2020
Admission routes3
Has abstractyes

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Same venueLe Naturaliste canadienSame topicFish Ecology and Management StudiesFrench-language works237,207