In-season assessment and management of salmon stocks using a Bayesian time–density model
Bibliographic record
Abstract
We document a time–density model for in-season assessment of salmon stocks that integrates both relative and absolute indicators of abundance and incorporates preseason information on run size and migration timing using a Bayesian framework. We evaluate different data collection programs for Fraser River sockeye salmon (Oncorhynchus nerka) by examining the precision, bias, and timeliness of resulting run-size estimates with a retrospective analysis of 20 years of data. We quantify the run-size bias if migration was early versus late and evaluate the impact of run-size uncertainty on the ability to reach management objectives. In-season assessments greatly improve the accuracy and precision of run-size estimates compared with preseason forecasts. For the in-season assessment of Fraser River sockeye, catch-per-unit-effort (CPUE) data from seaward marine test fisheries, although less precise, were more informative at the peak marine migration than more precise terminal, in-river hydroacoustic data obtained on the same date due to the time lag associated with migration from the marine test fishing locations to the lower river. Throughout the season, the best fisheries management results were obtained by relying on in-season assessments using both marine CPUE data as well as marine reconstructed abundance estimates derived from in-river hydroacoustic estimates, thereby taking advantage of the benefits of both sources of information.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".