Relationships between House Characteristics and Exposures to Metal(loid)s and Synthetic Organic Contaminants Evaluated Using Settled Indoor Dust
Bibliographic record
Abstract
This study investigates associations between house characteristics and chemical contaminants in house dust, collected under the nationally representative Canadian House Dust Study (2007−2010). Vacuum samples (<80 µm fraction) were analysed for over 200 synthetic organic compounds and metal(loid)s. Spearman rank correlations between contaminant concentrations in dust and presence of children and pets, types of flooring, heating styles and other characteristics suggested a number of indoor sources, pointing to future research directions. Numerous synthetic organics were significantly associated with reported use of room deodorizers and with the presence of cats in the home. Hardwood flooring, which is a manufactured wood product, emerged as a source of metal(loid)s, phthalates, organophosphate flame retardants/plasticizers, and obsolete organochlorine pesticides such as ∑DDT (but not halogenated flame retardants). Many metal(loid)s were significantly correlated with flame-retardant compounds used in building materials and heating systems. Components of heating appliances and heat distribution systems appeared to contribute heat-resistant chemicals and alloys to settled dust. Carpets displayed a dual role as both a source and repository of dust-borne contaminants. Contaminant loadings (<80 µm fraction) were significantly elevated in heavily carpeted homes, particularly those located near industry. Depending on the chemical (and its source), the results show that increased dust mass loading may enrich or dilute chemical concentrations in dust. Research is needed to improve the characterisation of hidden indoor sources such as flame retardants used in building materials and heating systems, or undisclosed ingredients used in common household products, such as air fresheners and products used for companion animals.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".