Considerations for using spawner reference levels for managing single- and mixed-stock fisheries of Atlantic salmon
Bibliographic record
Abstract
Abstract The probability of achieving the spawning requirement objective of Atlantic salmon (Salmo salar L.) is defined by the stochastic properties of small numbers and biological characteristics of the stock. The uncertainty in achieving the spawning escapement objective is greater for small stocks than for large ones, such that measures of annual performance are more variable for small stocks. Summing individual river spawner requirements into a regional requirement reduces the probability of meeting the objectives simultaneously in all rivers. Variations in productivity among stocks, when not accounted for, can result in under-escapement in areas of lower productivity. The impact of mixed-stock fisheries can be most important for small stocks, and especially if these are of low relative productivity. Increasing the regional spawner requirement in an attempt to compensate for lower productivity may alleviate the problem somewhat, but it is not a guaranteed solution to the challenge of protecting all stocks, specifically those of low productivity in mixed-stock fishery situations. Each mixed-stock fishery situation can be evaluated on a case-by-case basis using Monte Carlo techniques.
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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 | 0.029 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".