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Record W4313489028 · doi:10.1111/jfb.15298

A first glimpse of larval ecology of halibut species in the Gulf of St. Lawrence, Canada

2023· article· en· W4313489028 on OpenAlexafffundabout
Léopold Ghinter, Christophe Anderson, Dominique Robert, Gesche Winkler, Louis Bernatchez, Céline Audet

Bibliographic record

VenueJournal of Fish Biology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversité LavalUniversité du Québec à Rimouski
FundersFisheries and Oceans Canada
KeywordsHalibutGroundfishHippoglossus hippoglossusFisheryIchthyoplanktonEstuaryBiologyLarvaFisheries managementFisheries scienceEcologyFish <Actinopterygii>Fishing

Abstract

fetched live from OpenAlex

Knowledge of the larval ecology of winter-spawning fish from the Estuary and Gulf of St. Lawrence, Canada, remains scarce due to the seasonal ice cover that prevents ichthyoplankton sampling using conventional methods. Two winter-spawning species, Atlantic halibut (AH, Hippoglossus hippoglossus) and Greenland halibut (GH, Reinhardtius hippoglossoides), support the most important groundfish fisheries of this area. In March 2020, the authors captured 10 halibut larvae ranging in size from 5 to 14 mm during an opportunistic survey in the GSL onboard an icebreaking vessel. Of these, eight were AH and two GH. Judging by their very small size, the larvae were only a few days old, suggesting that the spawning grounds are close to the capture sites. This effort constitutes a first step in validating the putative spawning areas for these two important GSL stocks. This knowledge is important for the conservation and sustainable management of these fisheries.

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 score0.033
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.247
Teacher spread0.226 · 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

Citations5
Published2023
Admission routes3
Has abstractyes

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