Migration model of post‐smolt <scp>A</scp>tlantic salmon (<i><scp>S</scp>almo salar</i>) in the <scp>G</scp>ulf of <scp>M</scp>aine
Bibliographic record
Abstract
Abstract Understanding how oceanographic factors independently and interactively influence fish behavior, physiology, and survival is essential for predicting the impact of climate change on fish. Such predictions are especially challenging for highly migratory species such as salmon that experience a broad range of conditions. We applied a novel modeling approach that combines an individual‐based particle model with a bioenergetics model to evaluate the effects of oceanographic variability on migration of post‐smolt Atlantic salmon ( Salmo salar ). Interannual variability in the surface current velocity and sea surface temperature differentially influenced post‐smolt salmon migration. The magnitude, duration, and direction of the currents relative to a fish's intended swimming direction had the strongest influence on migration. Changes in ocean circulation led to changes in currents at a regional scale that have a similar, relative effect across multiple populations during out‐migration. Results of this study suggest that the Nova Scotia Coastal Current has a strong influence on the migration pathways of migrating salmon through the Gulf of Maine. The influx of cool fresh water from the Arctic, observed in the early 1990s, changed the Nova Scotia Coastal Current and, as suggested by model results, could have dramatically influenced post‐smolt salmon migration success. There was a trade‐off between arriving at the destination quickly but at a small size and not arriving at the destination at all. Fish that took a long time to migrate had more opportunities to feed and encountered warmer summer waters, increasing their overall growth.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".