Inshore and offshore marine migration pathways of Atlantic salmon post‐smolts from multiple rivers in Scotland, England, Northern Ireland, and Ireland
Bibliographic record
Abstract
The migratory behavior of Atlantic salmon (Salmo salar) post-smolts in coastal waters is poorly understood. In this collaborative study, 1914 smolts, from 25 rivers, in four countries were tagged with acoustic transmitters during a single seasonal migration. In total, 1105 post-smolts entered the marine study areas and 438 (39.6%) were detected on a network of 414 marine acoustic receivers and an autonomous underwater vehicle. Migration pathways (defined as the shortest distance between two detections) of up to 575 km and over 100 days at sea were described for all 25 populations. Post-smolts from different rivers, as well as individuals from the same river, used different pathways in coastal waters. Although difficult to generalize to all rivers, at least during the year of this study, no tagged post-smolts from rivers draining into the Irish Sea were detected entering the areas of sea between the Hebrides and mainland Scotland, which is associated with a high density of finfish aquaculture. An important outcome of this study is that a high proportion of post-smolts crossed through multiple legislative jurisdictions and boundaries during their migration. This study provides the basis for spatially explicit assessment of the impact risk of coastal pressures on salmon during their first migration to sea.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".