Assessing fishing policies for northeastern Brazil
Bibliographic record
Abstract
Abstract. This study is a first contribution towards the development of ecosystem-based fisheries management in northeastern Brazil, through the exploration of fishing policies based on a trophic model (Ecopath with Ecosim). Our simulations for 1978-2028 indicated that current fishing effort is completely unsustainable for lobsters (Panulirus argus (Latreille, 1804) and Panulirus laevicauda (Latreille, 1817)) and swordfish (Xiphias gladius Linnaeus, 1758). The simulation of optimum fishing policies led to a diverse fleet configuration when ecosystem health (ecological scenario) was emphasized. If the main objective was economic or social (or a combination of both and ecosystem health), manual gathering of coastal resources and demersal industrial fisheries could be boosted, while the lobster and longline fisheries should be phased out. A 50 % reduction in effort for lobster fisheries would not produce significant changes in lobster biomass; a reduction in effort to the 1978-level (fMSY – effort leval corresponding to the maximum sustainable yield) would lead to biomass recovery. An improvement in the collection system of fishery statistics (catch and effort) is recommended, as well as further gathering information on biological, economic, and social components of this ecosystem and its fisheries. Key words: ecosystem modelling, Ecopath with Ecosim, fisheries management, trophic model,
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".