A PESCA ILEGAL DO TAMBAQUI NOS RIOS NEGRO, SOLIMÕES E AMAZONAS: UMA ANÁLISE DOS EFEITOS DA LEI DO DEFESO
Bibliographic record
Abstract
Due to the decline in fish stocks in Brazil, mainly in the Amazon Basin, the Closed Fishing Season Law was implemented to help fisheries recover and protect fish habitat. Tambaqui ( Colossoma macropomum ) is one of the main overexploited fish species and there is concern about its decline in many Amazonian rivers. Therefore, in order to investigate the effectiveness of the Closed Fishing Season Law as it applies to tambaqui, seizure data compiled by IBAMA (Brazilian Institute of Environmental Protection) was analyzed for the period 1993 to 2012 (20 years) for the Negro, Solimoes and Amazonas rivers. Although the results of this study showed variations in the volume of tambaqui seizures among the three different rivers analyzed over a 20-year period, these variations were small in comparison to the differences found between the 10-year periods before and after implementation of the Closed Fishing Season Law. Overall, there was a 24.4% decrease in the volume of tambaqui seized after implementation of the Law, which can be attributed to a number of possible reasons: 1) reduction of tambaqui stocks in the area of study; 2) occurrence of great floods and dry periods influenced by the natural phenomena El Nino and La Nina, which strongly affected fish stocks in 2002-2003 and 2009, respectively; 3) poor enforcement of regulations, due to reductions in IBAMA personnel and; 4) difficult to patrol such a large area, including many remote locations. However, notwithstanding the noted overall decrease, the results suggest that implementation of the Closed Fishing Season Law with its corresponding compensation payments to fishers did not end up dramatically improving the protection of tambaqui stocks and may have even encouraged more people to enter into the fishery to benefit from the payments. Keywords: Fishery stocks; fisheries; closed season compensation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".