Monitoring Pesticide Use and Associated Health Hazards in Central America
Bibliographic record
Abstract
We established methods for monitoring pesticide use and associated health hazards in Central America. With import data from Belize, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Panama for 2000-2004, we constructed quantitative indicators (kg active ingredient) for general pesticide use, associated health hazards, and compliance with international regulations. Central America imported 33 million kg active ingredient per year. Imports increased 33% during 2000-2004. Of 403 pesticides, 13 comprised 77% of the total pesticides imported. High volumes of hazardous pesticides are used; 22% highly/extremely acutely toxic, 33% moderately/severely irritant or sensitizing, and 30% had multiple chronic toxicities. Of the 41 pesticides included in the Stockholm Convention on Persistent Organic Pollutants (POPs), the Rotterdam Convention on Prior Informed Consent (PIC), the Montreal Protocol on Substances that Deplete the Ozone Layer, the Pesticide Action Network (PAN) Dirty Dozen, and the Central American Dirty Dozen, 16 (17% total volume) were imported, four being among the 13 most imported pesticides. Costa Rica is by far the biggest consumer. Pesticide import data are good indicators of use trends and an informative source to monitor hazards and, potentially, the effectiveness of interventions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".