Preliminary results from an acoustic telemetry study on Atlantic Herring in the Northern Gulf of St. Lawrence
Bibliographic record
Abstract
In September 2021, 80 Atlantic herring were tagged with acoustic transmitters in Blanc-Sablon, Quebec, at the boundary of NAFO unit areas 4Ra and 4Sw. In 2022, an additional 30 herring were tagged in early May in Port-au-Port, Newfoundland (unit area 4Rc), followed by 79 more tagged in Blanc-Sablon in mid-September. To track the movements of these tagged fish, an array of 36 acoustic receivers was deployed along the Lower North Shore of Quebec (unit areas 4Sv and 4Sw) and the west coast of Newfoundland (division 4R). Preliminary results from September 2021 to November 2022 provide key insights into the timing and spatial dynamics of herring migration in the northeastern Gulf of St. Lawrence, closely reflecting the migration patterns described in earlier studies and aligning with the spatio-temporal trends observed in commercial fishery landings. Our findings reveal extensive seasonal migrations between summer feeding, spawning and overwintering habitats, with herring utilizing the Blanc-Sablon/Strait of Belle Isle area in summer and early fall, then migrating to areas along Newfoundland’s west coast, such as Bonne Bay, Bay of Islands, and deeper offshore waters, during the winter months, before returning northward in the spring and early summer. Notably, some herring appear to overwinter outside the previously identified Esquiman Channel, indicating the possibility of multiple overwintering grounds. These initial findings support the hypothesis that 4R and 4Sw stocks overlap seasonally, challenging the assumption of their discreteness and suggesting a potential need to consider 4R and 4Sw stocks as a single unit in future assessments. This study highlights the value of acoustic telemetry in capturing detailed, high-resolution movement data, which can be used to better understand habitat use and inform management decisions.
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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 | 0.001 | 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 | 0.001 | 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".