www.mdpi.com/journal/ijerph Do Questions Reflecting Indoor Air Pollutant Exposure from a Questionnaire Predict Direct Measure of Exposure in Owner-Occupied Houses?
Bibliographic record
Abstract
Abstract: Home characteristic questions are used in epidemiological studies and clinical settings to assess potentially harmful exposures in the home. The objective of this study was to determine whether questionnaire-reported home characteristics can predict directly measured pollutants. Sixty home inspections were conducted on a subsample of the 2006 population-based Toronto Child Health Evaluation Questionnaire. Indoor/outdoor air and settled dust samples were analyzed. Mean Fel d 1 was higher (p < 0.0001) in homes with a cat (450.58 µg/g) versus without (22.28 µg/g). Mean indoor NO2 was higher (p = 0.003) in homes with gas stoves (14.98 ppb) versus without (8.31 ppb). Self-reported musty odours predicted higher glucan levels (10554.37 µg/g versus 6308.58 µg/g, p = 0.0077). Der f 1 was predicted by the home’s age, but not by reports of carpets, and was higher in homesInt. J. Environ. Res. Public Health 2010, 7 3271 with mean relative humidity> 50 % (61.30 µg/g, versus 6.24 µg/g, p = 0.002). Self-reported presence of a cat, a gas stove, musty odours, mice, and the home’s age and
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.109 | 0.058 |
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".