Otoliths as chemical archives through ontogeny reveal distinct migratory strategies of Atlantic halibut within the Gulf of St. Lawrence
Bibliographic record
Abstract
Abstract In marine fishes of commercial interest, defining habitat use and migration strategies through ontogeny can help better understand the structure and dynamics of harvested populations and guide their management. The present study relied on otolith chemistry to identify three contingents within the Atlantic halibut (Hippoglossus hippoglossus) stock in the Gulf of St. Lawrence (GSL). We differentiated two chemical signatures from otolith edges, one for shallow (<100 m) and another one for deep (>100 m) waters. By identifying transitions between the deep and shallow habitats, we found that most halibut display migrations from the deep waters to shallow waters during the first 3 years of life. After reaching maturity, most halibut distributing in northern regions of the GSL became full-time residents in deep areas of the GSL. In contrast, halibut found in summer on the shallow plateau of the southern GSL displayed migrating behaviour between shallow (summer) and deep (winter) waters throughout their lives, either on an annual or irregular basis. Overall, our results demonstrate that otolith chemical signatures serve as natural markers of geographically distinct marine environments, facilitating the identification and reconstruction of environmental histories of long-lived marine fishes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".