MARINE PROTECTED AREA PERFORMANCE IN A MODEL OF THE FISHERY
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT. What bio‐economic benefits can be expected from the implementation of marine protected areas (MPAs) in a fishery facing a shock in the form of recruitment failure, and managed jointly compared to separately? What are the optimal sizes of MPAs under cooperation and non‐cooperation? I explore these questions in the current paper by developing a computational two‐agent model, which incorporates MPAs using the North East Atlantic codfishery as an example. Results from the study indicate that MPAs can protect the discounted economic rent from the fishery if the habitat is likely to face a shock, andfishers have a high discount rate. The total standing biomass increases with increasing MPA size but only up to a point. Basedon the specifics of the model, the study also shows that the economically optimal size of MPA for cod varies between 50 70% depending on (i) the exchange rate between the protectedandunprotectedareas of the habitat; (ii) whether fishers behalf cooperatively or non‐cooperatively; and(iii) the severity of the shock that the ecosystem may face.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it