Harvest control rules for mixed-stock fisheries coping with autocorrelated recruitment variation, conservation of weak stocks, and economic well-being
Bibliographic record
Abstract
Dynamic programming is used to construct harvest control rules that account for persistent changes in productivity, exploitation rate constraints that prevent extinction of nontarget weak stocks, and an economic objective that recognizes moderate income to be more important to fishermen than maximization of total profit. Persistent productivity changes imply downward adjustment in spawning abundance targets during periods of low productivity, while conservation constraints simply imply upper limits on exploitation rate at high stock sizes. When the economic objective is to maximize the logarithm of net income (diminishing marginal utility or welfare from higher incomes), the optimum control rule shifts from a fixed escapement form to a curve where exploitation rate increases smoothly from zero at the minimum stock size that can be fished profitably to the upper limit set by a conservation constraint. This policy is not the fixed-exploitation rate form that has been historically suggested as a way of stabilizing harvests without major loss in profits. Application of harvest control rules constructed using dynamic programming in a mixed-stock salmon fishery in fact results in total profits close to those obtainable with fixed escapement policies but without the frequent low catches or closures implied by fixed escapement policies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".