Characterization of pesticide consumption in the county of Santarém, Pará, Brazil
Bibliographic record
Abstract
Many potentially harmful pesticides for both human health and the environment are used in Brazilian Amazon. However, no scientific datum on pesticide usage is presently available for this region. Consequently, it is difficult to assess which substances arc used and in which quantities. As an important premise for future work on pesticide contamination in the county of Santarém (State of Pará, Brazil), a survey was conducted in order to qualify and quantify the use of some pesticides in this region. This investigation was made between January and March 1997 and August and October 1998 and revealed use of several organophosphates, synthetic pyrethroids and carbamates insecticides. Furthermore, many herbicides and fungicides were listed. These pesticides are used for agriculture, domestic, and sanitary programs. This paper also provides a first estimation of quantities of some insecticides commonly used in agriculture (chlorpyrifos, malathion, metamidophos and methyl-parathion). The annual consumption for these four compounds is estimated at ca. 1 910 kg. Organophosphate insecticide consumption in the county of Santarém seems to be lower than the Brazilian average in terms of «per capita» and «per agricultural area» consumptions. Nevertheless, this county uses toxic substances on sensitive environments such as floodplains (várzeas), making relevant a thorough study on the potential contamination of this environment and its biota.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".