Stock-scale electronic tracking of Atlantic halibut reveals summer site fidelity and winter mixing on common spawning grounds
Bibliographic record
Abstract
Abstract Knowledge of movement ecology, habitat use, and spatiotemporal distribution is critical to inform sustainable fisheries management and conservation. Atlantic halibut in the Gulf of St. Lawrence (GSL) is of great economic value, although little is known about its spatiotemporal distribution, seasonal migrations, and spawning areas. To investigate these aspects, 114 pop-up satellite archival tags (PSATs) were deployed on halibut from 2013 up to 2018 throughout the GSL. A total of 62 physically recovered PSATs provided complete archived datasets with high temporal resolution. PSAT detachment locations revealed specific summer site fidelity. In contrast, the reconstruction of movement tracks with a geolocation model revealed that all fish converged to the Gulf’s deep channels to overwinter and spawn. This suggests strong mixing during the spawning period and thus one reproductive population within the GSL. These findings illustrate the utility of large-scale PSAT tagging combined with dedicated PSAT-recovery surveys to reveal critical stock-scale information on movements and spawning locations. This information addresses important gaps in the movement ecology of this halibut stock, revealing that reported summer site fidelity, based on years of conventional tagging, also conceals important winter mixing that is only apparent through analyses of movement on the time scale of annual cycles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".