A Bayesian decision analysis to set escapement goals for Fraser River sockeye salmon (<i>Oncorhynchus nerka</i>)
Bibliographic record
Abstract
This paper illustrates a complete Bayesian decision analysis for evaluating multistock harvest goals in the fishery on Fraser River sockeye salmon (Oncorhynchus nerka). We identify four key steps necessary to assess a resource production system. Each step entails choices that can alter the perceived consequences of management decisions. A Markov chain Monte Carlo sample captures uncertainty in the population dynamics. The Bayesian formalism then translates this uncertainty into uncertain policy outcomes. We examine a relatively simple control law, designed to protect stocks at low abundance. We restrict our attention to retrospective policy analysis by investigating what might have happened to sockeye stocks if management had proceeded differently during years for which historical data are available. A formal objective function quantifies societal values associated with a range of policy options. To confine the paper to manageable scope, we consider only relatively simple assumptions. Our analytical framework offers an iterative route to policy design, where managers play an active role in formulating policy options and evaluating their consequences.
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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 | 0.012 | 0.025 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".