Application of the Method Evaluation and Risk Assessment Tool for a Small-Scale Grouper Fishery in Indonesia
Bibliographic record
Abstract
Management strategy evaluation using the Method Evaluation and Risk Assessment (MERA) platform was used to evaluate management procedures (MPs) for improving the management of the leopard coral grouper (Plectropomus leopardus) fishery in Saleh Bay, Indonesia. This grouper is a valuable species currently under high fishing pressure. It is targeted by small-scale fisheries using a wide range of fishing methods; hence, management recommendations are needed to ensure sustainability. A suite of MPs for data-limited conditions were evaluated for their ability to achieve limit and target biomass reference points (B/BMSY = 0.5 and B/BMSY = 1, respectively), while maintaining a target yield of at least 0.5 MSY. The simulation results suggest that the currently implemented harvest control rules (HCRs) in Saleh Bay (size limit and spatial closure) may not be effective in achieving the management objective to attain the target biomass reference point due to relatively low compliance with the size limit regulation (320 mm total length) and the very small proportion of existing MPA no-take areas (~2.2%). This study recommends that the fisheries management authority explores the feasibility of implementing the total allowable catch (TAC) and seasonal closure in addition to the existing fishing regulations for P. leopardus in Saleh Bay.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".