Legislation of Pesticides in Citriculture, Community of Cubiteua, Capitão Poço/PA
Bibliographic record
Abstract
The Brazilian citriculture presents hegemony in the production and export of orange juice. The state of São Paulo is the largest national producer, accounting for 74% of the national production of this fruit. The State of Pará is responsible for 1.02% of the production of Orange in Brazil, of that amount the municipality of Captain Poço is responsible for 57% of the total produced. The objective of this research was to analyze the profile of different producers in compliance with the pesticide legislation. For the development of the work, a survey was carried out based on the Law of Agrochemicals—Law No. 7,802 of July 11, 1989 and the Law of Packaging—Law No. 9,974 of June 6, 2000, in the community of Cubiteua belonging to the municipality of Captain Poço/PA. The producers interviewed were chosen based on planted area and these were typified according to the amount of citrus planted. The percentage of respondents who know Federal Law 7,802/89, rectified by Law 9,974/00 and regulated by Decree No. 4,074/02, is equal to 8%. Both laws are not known to most producers, especially those with the smallest planted area, and as a consequence, they are not being met, leading to environmental, social, economic and human health problems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".