Tributary-specific variation in timing of return of adult Atlantic salmon (<i>Salmo salar</i>) to fresh water has a genetic component
Bibliographic record
Abstract
Variation in the timing of return of adult Atlantic salmon (Salmo salar) to rivers contributes diversity to salmon fisheries and is therefore an important effect on their economic value. In this paper, we test two sequential null hypotheses: (i) that run timing does not vary among populations from different tributaries and (ii) that differences in time of return between populations from different tributaries are not evident following transfer to a common location. Adult fish originating from two tributaries of the River Tay, the Almond and Tummel, were caught in local fisheries on different dates. For both tributaries, run timing in two-sea-winter (2SW) fish was earlier than for one-sea-winter (1SW) fish. Within both sea-age classes, recaptures of Tummel fish preceded those from the Almond. After the progeny of Almond and Tummel fish were transferred to a third tributary of the River Tay, the Braan, differences in run timing between the tributary groups were identified for both sea-age classes. The differences were highly consistent with those present in fish that had reared in their native tributaries, indicating a strong heritable component for the run timing trait. Tributary-specific differences in run timing have important implications for the management of seasonally complex fisheries.
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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 | 0.001 | 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) | 0.001 | 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".