Important open-ocean areas for northern Atlantic salmon (<i>Salmo salar</i>) — as estimated using a simple ambient-temperature approach
Bibliographic record
Abstract
Tracking salmon migratory behaviour in the open ocean has been a challenge to researchers. As the marine phase essentially determines the size and survival of individuals and populations, it is arguably the most influential life cycle period for salmon population dynamics. Thus, methods providing an understanding of the spatial and temporal patterns of salmon marine migratory behaviour could improve the species' management and conservation. A model was developed that correlated temperature data from archival tags with sea-surface temperatures (SSTs) to identify the probable marine feeding areas of a northeastern Atlantic salmon (Salmo salar) population over 3 years. The marine distribution of the tagged population extended from the Greenland Sea, north to Svalbard, and into the eastern Barents Sea. Higher probability occupancy zones overlapped with the polar front area from September to April during all 3 years. While the migratory behaviour appeared similar between years and seasons, the fish were distributed farther south and west during the autumn of 2007 than during the autumns of 2006 and 2008. This may have been related to warmer summer SSTs and an earlier annual maximum SST. The ambient-temperature approach developed here is a cost-effective way to monitor the open-ocean migratory patterns of surface-oriented 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 | 0.001 |
| 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 | 0.001 | 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".