Monitoring Pesticide Use and Associated Health Hazards in Central America
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it