Missing the target: uncertainties in achieving management goals in fisheries on Fraser River, British Columbia, sockeye salmon (<i>Oncorhynchus nerka</i>)
Bibliographic record
Abstract
In sockeye salmon (Oncorhynchus nerka) fisheries, management targets are rarely achieved exactly, thereby creating uncertainties about outcomes from implementing fishing regulations. Although this type of uncertainty may be large, it is seldom incorporated into simulation models that evaluate management options. One objective of this study was to quantify the deviations that occur between realized and target mortality rates (i.e., the target fraction of adult recruits that die each year during return migration, mostly due to harvesting) in fisheries for sockeye salmon from the Fraser River, British Columbia. We found that for some sockeye stocks, realized mortality rates were higher than targets when recruitment was low (resulting in conservation concerns) and lower than targets when recruitment was high (resulting in foregone catch). Scientists and managers can at least partially account for effects of such deviations between realized and target mortality rates (outcome uncertainties) by choosing target harvest rules that reflect typical patterns in those deviations. We derived a method to permit modelers to incorporate those patterns into analyses of management options.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".