Design Factors and Socioeconomic Variables Associated with Ecological Responses to Fishery Closures in the Western Indian Ocean
Bibliographic record
Abstract
We assessed the ability of socioeconomic variables (population size, perceived infringement, and community infrastructure) and design features (closure age and area) to predict ecological indicators of “success” in seventeen coral-reef fishery closures in the Western Indian Ocean. Success was measured as absolute fish biomass and coral cover in closures, and the response ratio of these variables indicating the level of difference relative to control sites outside closures. Fish biomass had a greater and more consistent response to protection than coral cover. Human population density had a strong positive association with the response of fish biomass, which was driven by lower biomass outside marine protected areas (MPAs) in high human population density sites, rather than higher biomass within MPAs. Perceived infringement was negatively associated with the absolute and response ratio of fish biomass. Coral cover was variable, weakly related to closure, and positively related to closure size and human population density. This, and previous regional studies, indicate that physical design features have a modest effect on the response of fish to MPAs, that design features interact with compliance, and that human population density around closures can indirectly influence the effect of MPAs on fish populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".