An indicator‐based decision framework for the northern California red abalone fishery
Bibliographic record
Abstract
Abstract Among abalone species that were once harvested along the California coastline, red abalone (Haliotis rufescens) supports the remaining recreational fishery. To support development of a red abalone fishery management plan, non‐governmental organizations have initiated expanded data collection and developed fishery management strategies. The latter is the subject of this study, as we present a management strategy evaluation (MSE) of a multi‐indicator decision tree. The decision tree relies on landings from each of 56 fishing sites and length frequency information collected during fishery‐independent diver surveys at a subset of sites. The decision tree was designed to cope with existing data limitations and to ensure that localized meta‐population dynamics were adequately considered in decision‐making. It was also necessary to balance the potential for localized abundance changes with the practical issue of implementing fishery regulations at larger spatial scales. The MSE demonstrated that undesirably low stock sizes could be avoided while also continuing to maintain a viable fishery, even under environmental conditions that are detrimental to abalone populations. Under less‐severe environmental conditions, stock size was maintained, on average, above the biomass associated with production of maximum sustainable yield. Our discussion centers on steps that were taken to refine the decision tree and to incorporate feedback from scientists and stakeholders and to facilitate transparent evaluation of management options.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".