Distribution and biological characteristics of escaped farmed salmon in a major subarctic wild salmon river: implications for monitoring
Bibliographic record
Abstract
We report the occurrence, distribution, and biological characteristics of escaped farmed salmon in the River Teno in northernmost Europe, which supports one of the largest and most versatile wild Atlantic salmon ( Salmo salar ) populations in the world. Farmed salmon were caught during the fishing season (May–August) when their proportion in the catch varied between 0.0% and 0.7%. Occasional sampling after the fishing season revealed much higher proportions of escapees, up to 47%, indicating a potential for a more severe impact of farmed fish than the in-season monitoring is able to uncover. Peak migration of the wild salmon was in June or July, but that of escaped farmed fish was in August. Up to 88% of the escaped salmon caught in August showed gonad development, and scale analysis indicated that 4.5% of them were repeat spawners. Genetic analyses using microsatellite markers revealed highly significant genetic differentiation between wild salmon and escaped farmed fish (F ST = 0.05) caught in the River Teno and a Norwegian farmed strain (F ST = 0.10). The heterogeneity of escapees compared with the single farmed strain indicated that escapees apparently originate from multiple sources, which will complicate their genetic identification and the assessment of the level of hybridization with wild salmon.
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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 | 0.000 |
| 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".