Simulations of the impact of different temporal and spatial allocations of fishing effort on fishing mortality in a lobster (<i>Homarus americanus</i>) fishery
Bibliographic record
Abstract
A spatially explicit model is proposed to assess the impact on fishing mortality of modifying effort patterns for an American lobster (Homarus americanus) fishery. A two-box (offshore and inshore grounds) model is developed for the 1995 lobster fishery season in the Magdalen Islands (Quebec). It considers lobster migration and fisher's temporal and spatial effort dynamics to estimate within-season catchability patterns and exchange rates between the two spatial units. Different management scenarios are simulated: reducing nominal fishing effort and changing its temporal (season's length) and spatial (area closures) allocations. Catchability showed a strong temporal trend, being highest during the first 3 weeks and declining regularly afterwards. The model indicated a continuous migration toward the inshore during the fishing season and that a significant amount of biomass remained offshore. As a result, reducing fishing effort at the beginning of the season would have the greatest impact on exploitation rate. Allowing less effort in the offshore area would also reduce the exploitation rate significantly. Restricting effort to the inshore area, as it was 25 years ago, reduced substantially the exploitation rate. This model represents the first attempt to analyze in-season fishery dynamics and should be useful to further assess the impact of management measures.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".