An evaluation of acceptable biological catch (ABC) harvest control rules designed to limit overfishing
Bibliographic record
Abstract
In this paper we developed a simulation model to evaluate a range of acceptable biological catch (ABC) control rules to determine their relative performance at achieving common fishery management objectives. We explored a range of scenarios to determine robustness of a control rule to different situations and found that across scenarios the control rules that used a buffer to account for scientific uncertainty when setting the ABC were able to limit the frequency of overfishing. Modest buffers when setting the ABC were generally effective at limiting overfishing, but larger buffers resulted in higher average biomass, similar long-term benefits to the fishery (high yield, low variability in yield), more rapid recovery of depleted populations, and a lower risk of the population being overfished, and these results were robust to the level of uncertainty in the assessment model estimates. In addition, fixing the ABC over the interval between assessments and having a short interval between assessments was generally more effective at meeting management objectives than using projections and having a long assessment interval.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".