Bibliographic record
Abstract
The utility of traditional bio-economic harvest models suffers from their dependence on two commonly used approaches.First, optimization is often assumed for harvester behavior despite system complexity and the often neglected costs associated with information gathering and deliberation.Second, ecosystem interactions are infrequently modeled despite a growing awareness that these interactions are important.This paper develops a simulation model to examine the consequences of harvesting at two trophic levels in a coral-reef food web.The model assumes adaptive rather than optimizing behavior among fishermen.The consequences of changing economic, biological, and social parameters are examined using resilience as an evaluative framework.Three general conclusions are reached.First, the simulated ecosystem is sensitive to small changes in economic, biological, and social parameters.Second, threshold effects are common.Third, as compared to results typical of traditional single-species optimization models, some results are counter-intuitive.Benefits of this approach are that the model affirms and adds to the results of traditional bio-economic harvest models, is empirically operational, and provides a richer selection of policy alternatives.Finally, the analysis of trade-offs in terms of resilience provides a useful evaluative framework for multiple-species harvest models.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".