Catchability of Atlantic salmon at high water temperatures: Implications for river closure temperature thresholds to catch and release angling
Bibliographic record
Abstract
Abstract Warming water temperatures, combined with increased mortality following catch and release, could have synergistic consequences if rivers remain open to catch and release at high water temperatures, and catchability of fish remains similar across water temperatures. Here archived data for Atlantic salmon, Salmo salar L., were used to (a) quantify the influence of water temperature on catchability and (b) refine estimates of absolute catch and release mortality to incorporate the relationship between temperature and catchability. A significant decline in the number of Atlantic salmon caught at warmer water temperatures was found after accounting for the effects of river water height, fishing effort, run duration and year‐to‐year differences in fish abundance. Overall, absolute catch and release mortalities were predicted to be infrequent at cool river temperatures. At river temperatures sometimes associated with fishing closures, mortality due to the catch and release ranged from 6% to 14%. Although post‐release mortality increases with water temperature, it is somewhat compensated by the reduced catchability of Atlantic salmon. Thus, the catchability component of catch and release is an integral consideration when evaluating the effectiveness of river closure temperature thresholds when managing catch and release angling.
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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 | 0.003 |
| 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".