Pesticide Risk Reduction on Crops in the Province of Ontario
Bibliographic record
Abstract
We analyzed the changes in pesticide use and risk in the Province of Ontario, Canada, from 1973 to 1998 to monitor the success of Food Systems 2002, a program to reduce pesticide use by 50%. Pesticide risk was calculated by multiplying the amount of pesticide used (kilograms of active ingredient) by the Environmental Impact Quotient (EIQ), a score for the potential risk of pesticides to farmworkers, consumers, and the environment. Pesticide use increased by 46% from 1973 to 1983. From 1983, the baseline year for Food Systems 2002, to 1998, pesticide use decreased by 38.5% and risk declined 39.5%. The reductions in pesticide use and risk were primarily on corn (Zea mays L.) and tobacco (Nicotiana tabacum L.), the crops with the highest pesticide use in 1983. Total pesticide use on soybean [Glycine max (L.) Merr.] did not change, but the mean application rate (kg ha(-1)) decreased by 57%. Corn and soybean account for 65% of pesticide use, but have a relatively low pesticide use and risk per hectare and per tonne of production. Total pesticide use on tobacco, fruits, and vegetables was lower than on corn or soybean, but the pesticide use and risk per hectare were much higher. Small reductions in pesticide use on corn and soybean may allow a 50% reduction in pesticide use, but greater reductions in risk can be achieved by reducing the use of "high risk" pesticides on fruit and vegetables.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".