Human Exposure to Malathion during a Possible Vector-Control Intervention against West Nile Virus. I: Methodological Framework for Exposure Assessment
Bibliographic record
Abstract
The arrival of West Nile Virus in North America prompted public health authorities to develop intervention plans in order to prevent the propagation of this mosquito-transmitted pathogen. The last-resort measure proposed by the Government of Quebec (Canada) is the large-scale application of insecticides by aerial or ground Ultra Low Volume (ULV) treatment. This article presents an assessment of the exposure to malathion and its metabolite, malaoxon, for a population where an eventual application of malathion would occur. Each exposure pathway is detailed by describing the equation and every conservative assumption. This methodological framework was then used with the aim of assessing the toxicological risk based on a probabilistic approach (see companion article, this issue). In the current study, a daily absorbed dose of the mixture of malathion and malaoxon was estimated in terms of “malathion equivalent dose.” Each exposure pathway following a single event of ground or aerial ULV spraying of malathion was investigated for the population, classified into five age groups. Dermal exposure to dislodgeable residues on turf, foliage, and hard surfaces was estimated to be the most important source of exposure compared to any other pathways; it accounts for 63% to 98% of the estimated cumulative absorbed doses. The hand-to-mouth behavior of toddlers may also contribute considerably to their “malathion-equivalent” exposure (i.e., approximately 15% of the cumulative dose). Otherwise, the current study shows that ground ULV spraying is the type of treatment that may induce lower exposure, because the predicted concentration of malathion on turf and foliar surfaces is less than the one predicted for aerial ULV spraying.
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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 | 0.054 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".