Winter growth and survival of juvenile Atlantic salmon (<i>Salmo salar</i>) in experimental raceways
Bibliographic record
Abstract
We used experimental raceways to determine overwinter mortality of wild-reared immature and mature post-young-of-the-year Atlantic salmon (Salmo salar). Secondarily, we investigated the effects of differing treatments (velocity and shelter) on winter growth and survival. Overall survival from November to April was 94%, and survival of immature (98%) and mature (90%) parr, although statistically different, was very similar. Immature parr grew more in length than mature parr, and both immature and mature parr in higher velocity (12 cm·s1) raceways grew more than those in lower velocity (0.6 cm·s1) raceways. Stomach contents were twofold greater in parr occupying higher velocity raceways than those in lower velocity raceways. Caloric content of immature and mature parr did not differ in any of five monthly samples. Lowest caloric content occurred in early February and increased between February and March when water temperatures were well below those considered optimal for growth. Although ice cover was present, isolating parr from conditions that occur in natural settings may have helped parr achieve nearly 2.5 times greater survival than parr in the wild. Further, whereas previous studies showed parr select habitats to minimize energetic loss, our results show a distinct advantage for parr to expend energy to feed during winter.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".