The use of IRPeQ model as indicator to estimate the risk of some pesticides onhuman health and environment. Bouagga, A., Chaabane, H., Bahrouni, H., andHassine K
Bibliographic record
Abstract
Different pesticides can be used by farmers to control bioagressors. To assess environmental and human health risks due to pesticide use, pesticide risk indicators are elaborated. The objective of this study was to characterize the potential risks of pesticides used and their side effects on health and environment. A census of the use of pesticides in citrus orchards was conducted among farmers in Tunisia during crop season 2013/14. Two risk indexes were calculated for each pesticide: a Health Risk Index (HRI) and an Environmental Risk Index (ERI) according to the Quebec Pesticides Risk Indicator (IRPeQ). The parameters used to follow each calculation were obtained from the registration dossier of each formulation and the international databases like Agritox, Extoxnet and PAN Pesticides. The highest HRI were obtained for the formulations based on the active ingredient methidathion (HRI= 1227), while, Success Appât ® a formulation, based on spinosad as active ingredient, is the product with the lowest health risk (HRI= 12). On the other hand, its ERI was evaluated with an index of 175, according to its toxicity towards honey bees. The fungicide formulation Aliette Express® presented slightly low health and environmental risk indexes. The determination of the risk indexes HRI and ERI allowed us to compare pesticides (active ingredient and formulation) according to their potential risk and facilitate the choice of the pesticide with least risk for human health and environment.
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.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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 teacher head, 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".