Comment on "Fishing and the impact of marine reserves in a variable environment"
Bibliographic record
Abstract
Rodwell and Roberts (Can. J. Fish. Aquat. Sci. 61: 20532068 (2004)) use a discrete time simulation model to investigate the impact of marine reserves on catch and biomass levels in a fishery regulated by total catch quotas. They show that the probability of achieving various probabilistic biomass reference point targets is greater with reserves than without reserves under equal nonreserve exploitation rates. However, they compare management performance on the basis of exploitation rates of the nonreserve biomass rather than average yields (i.e., not using the same overall exploitation rate for the population as a whole). What Rodwell and Roberts fail to show is that with their model all of the reference point targets can be achieved with a higher level of catch without a reserve than with a reserve. For any given level of mean catch at or below maximum sustainable yield, management without a marine reserve leads to a higher average biomass and lower variation in catch than management with a reserve. In summary, the specific model and parameters used by Rodwell and Roberts fail to show any comparative advantage of marine reserves in a quota-managed fishery.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.051 | 0.037 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".