Ecosystem models for management advice: An analysis of recreational and commercial fisheries policies in Baja California Sur, Mexico
Bibliographic record
Abstract
Recreational fishing is a vital component of the tourism economy in Baja California Sur (BCS), Mexico, although several artisanal and industrial fisheries continue to operate in the region. The commercial long-liner fleet in particular is widely held to be responsible both for diminishing shark populations and declines in billfish through bycatch. Using available fisheries and ecosystem data, we develop an Ecopath with Ecosim (EwE) model to represent current ecosystem and fishing dynamics in BCS and explore the ecological and economic effects of specific fisheries policy measures. Results suggest that currently mandated bycatch limits for the longlining fleet will have little effect on marlin abundance in the area. In an overfished ecosystem, decreasing fishing effort can result in higher overall catches through population rebuilding. While perhaps ecologically justified, increases in the abundance of sharks, a top predator, can have negative effects on other valued species in the ecosystem. The effects of these trophic dynamics must not be overlooked, as they can negate or even reverse desired outcomes from fisheries management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".