Setting public health guidelines for chemicals in indoor settled dust. First achievements and steps to come: The case of lead
Bibliographic record
Abstract
Health based guidelines for environmental concentrations of chemicals are designed to prevent harmful chemical exposures. However, none exists for indoor settled dust, despite its ingestion being a documented pathway of exposure, for certain chemicals. The objective of this paper is to present the derivation of a health-based indoor settled dust guideline (ISDG) for lead. The guideline was developed to protect a specific fraction of the most vulnerable population against the most sensitive effect, taking into account other exposure pathways. It is calculated from a toxicological reference value, body weight, and the mass of ingested dust. The most vulnerable population is young children, and the corresponding critical effect is a loss of IQ points, with a toxicity reference value of 0.5 µg.kg bw −1 .d −1 . Assuming that 80 % of the exposure for the most affected individuals comes from dust ingestion, the ISDG for protecting 90, 95 or 95 % of young children are 43, 33 and 20 µg.g dust −1 , respectively. These values are consistently lower than the concentrations that would trigger lead poisoning screening. The main uncertainties lie in the estimations of the amount of ingested dust. This ISDG could contribute to environmental management efforts to prevent or reduce lead exposures. • Health-based indoor settled dust guideline were calculated for lead. • Computed from toxicity and exposure data. • Computed for protection of 90,95 & 99 % of vulnerable population. • Complete other reference values for lead in dust.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".