Comparing size-limit and quota policies to increase economic yield in a lobster fishery
Bibliographic record
Abstract
To advance economic and sustainability objectives in a lobster fishery, four broadly different management policies were evaluated: minimum and maximum size limits, constant catch quotas, and quota set yearly in proportion to the previous year’s catch per unit effort (CPUE). The performance of each policy was evaluated based on its discounted economic yield, together with egg production, catch, and catch stability. Maximum size limits performed poorly for all indicators. Raising the minimum size increased economic yield by improving yield-per-recruit. Output controls, both constant and dynamic, uniformly outperformed size limits, leading to substantially higher economic yield and egg production. A dynamic harvest control rule, setting quota in proportion to the previous year’s catch rate, achieved the highest economic yield, catch, and egg production over 20 years. The optimal (30%) exploitation rate under this policy produced a 182% improvement in economic yield compared with a baseline strategy of only minimum size, but led to a mean year-to-year change in quota of 11.5% in response to yearly variable recruitment. This quota-setting management regime is straightforward to implement, using only catch rate as input. When absolute exploitation rate estimates are not available, this quota-setting harvest control rule can be constructed using only a target level of effort.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".