Determinants of riverine migration success by Atlantic salmon (<i>Salmo salar</i>) smolts from rivers across the <scp>UK</scp> and Ireland
Bibliographic record
Abstract
Abstract There is some evidence that the river migration success of Atlantic salmon smolts, on their first migration to sea, varies both spatially and temporally. However, we have only a poor understanding of what may be driving this variation. In this study, we used acoustic telemetry to quantify the spatial and temporal variations in river migration success in Atlantic salmon smolts on their first migration to sea. In total 4120 Atlantic salmon smolts migrating through 22 rivers in Scotland, England, Ireland, and Northern Ireland over multiple years were included in the study. Individuals were defined as successful migrants if detected leaving the river to enter marine waters. The results show significant temporal (up to 4 years) and spatial (river) variations in migration success, with overall between‐river migration success varying from 3.4% to 97.0% and between years from 3.4% and 61.0%. Temporal variation in migration success was river specific, with some rivers being more temporally stable (exhibiting little variation between years) than others. Across all rivers and years, individual migration success was predicted positively by body condition and negatively by tag burden. The rate of migration success for a population (migration success standardized to a common river distance [proportion km−1]) was predicted by a number of environmental factors. The proportion of river catchment that comprised wetland and woodland positively predicted migration success, whereas the proportion of grassland and peatland in a catchment negatively predicted the rate of migration success. Although the mechanisms through which these effects may be operating were not directly examined in this study, we discuss some potential routes through which they may occur.
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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 | 0.000 |
| 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".