Does river of origin influence the early marine migratory performance of <i>Salmo salar</i>?
Bibliographic record
Abstract
The early marine migratory behaviour of two populations of hatchery-reared Atlantic salmon Salmo salar was compared in a common-garden experiment. Post-smolts from a river in a long fjord (Laerdal River, 144 km from the open coastline, n = 79) and a short fjord (Flekke River, 20 km from the open coastline, n = 80) in western Norway were tagged with acoustic transmitters and released during the spring of 2005 and 2006 in the inner part of the Hardangerfjord system (Opo River mouth, 179 km from the open coastline). The migratory behaviour of the tagged fish was monitored by acoustic listening stations in the fjord system up to 167 km from the release site. The Laerdal fish began migrating before the Flekke fish and had higher progression rates in the middle part of the fjord system. A greater number of Laerdal fish was detected along the most direct migratory route and in the outermost part of the Hardangerfjord system, which is indicative of a higher survival. The results from this study demonstrate differences in early marine migratory behaviour between S. salar from two different stocks and suggest that the distance a S. salar population travels to reach the open coastline may influence its early marine migratory behaviour and performance. The selective pressures of marine predation and arrival time at feeding areas in the ocean may be stronger for stocks with a longer inshore migration, creating more efficient migrants over time.
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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 | 0.000 | 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".