Ethnobiology of snappers (Lutjanidae): target species and suggestions for management
Bibliographic record
Abstract
In this study, we sought to investigate the biology (diet and reproduction) and ethnobiology (fishers knowledge and fishing spots used to catch snappers) of five species of snappers (Lutjanidae), including Lutjanus analis, Lutjanus synagris, Lutjanus vivanus, Ocyurus chrysurus, and Romboplites saliens at five sites along the northeast (Riacho Doce, Maceió in Alagoas State, and Porto do Sauípe, Entre Rios at Bahia State) and the southeast (SE) Brazilian coast (Paraty and Rio de Janeiro cities at Rio de Janeiro State, and Bertioga, at São Paulo State.).We collected 288 snappers and interviewed 86 fishermen. The stomach contents of each fish were examined and macroscopic gonad analysis was performed. Snappers are very important for the fisheries of NE Brazil, and our results indicated that some populations, such as mutton snapper (L. analis) and lane snapper (L. synagris), are being caught when they are too young, at early juvenile stages.Local knowledge has been shown to be a powerful tool for determining appropriate policies regarding management of target species, and artisanal fishermen can be included in management processes. Other suggestions for managing the fisheries are discussed, including proposals that could provide motivation for artisanal fishermen to participate in programs to conserve resources, such as co-management approaches that utilize local knowledge, the establishment of fishing seasons, and compensation of fishermen, through 'payment for environmental services'. These suggestions may enhance the participation of local artisanal fishermen in moving to a more realistic and less top-down management approach of the fish population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".