Characterization of Depth Distributions, Temperature Associations, and Seasonal Migrations of Atlantic Halibut in the Gulf of St. Lawrence using Pop-Up Satellite Archival Tags
Bibliographic record
Abstract
Abstract The fishery for Atlantic Halibut Hippoglossus hippoglossus in the Gulf of St. Lawrence (Gulf) is currently experiencing its highest landings since the 1950s. However, the absence of information on adult habitat use has limited the development of new survey methodologies. The aim of this study was to use pop-up satellite archival tags on large (≥108-cm) halibut in the Gulf to provide data on seasonal temperature associations, depth distributions, and migrations. Twenty Atlantic Halibut were tagged in 2013 and 15 were tagged in 2015 at two different locations in the northern Gulf. Atlantic Halibut overwintered in the central and northern Gulf based on six tag pop-offs. In the winter in both studies, halibut were distributed at 160–440 m depth with a narrow temperature association of 5.5–6.5°C, which corresponded with the bathymetry and hydrography of the Gulf rather than the deeper and colder waters of the continental shelf where the southern stock occurs. Spawning rises were identified from the depth data for two halibut in 2013 and six halibut in 2015. In the summer, halibut had a wide range of depth (20–200 m) and temperature (−1.5°C to +15.0°C) associations, and tag pop-offs in August indicated feeding grounds off the northwestern coast of Newfoundland. There was a broad geographic distribution of final pop-offs in 2013, but in 2015 all tags popped off close (<55 km) to the original tagging site. These seasonal depth distributions and temperature associations will be used to inform Atlantic Halibut stock assessments and 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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".