Implementation of the indoor air component of cycle 2 of the Canadian Health Measures Survey.
Bibliographic record
Abstract
BACKGROUND: The presence of volatile organic compounds (VOCs) in indoor air may have negative health consequences, ranging from mild irritation to more severe illnesses. Indoor air data are required to assess Canadian population exposure to these VOCs. DATA AND METHODS: The 2009 to 2011 Canadian Health Measures Survey (CHMS) included an indoor air component. Respondents who went to the mobile examination centre to participate in the physical measures section of the survey were asked to deploy an indoor air sampler in their homes for 7 consecutive days. Data were collected for 84 VOCs. Control samples that were implemented included duplicates and blanks. RESULTS: Of the 4,686 indoor air samplers given to CHMS respondents, 4,581 were deployed and returned to the testing laboratory. Data from 3,857 samplers met the criteria for inclusion in the CHMS indoor air data files. Thirteen VOCs had a mean percentage difference between the duplicate pairs greater than 30%. The field and cleaning blank geometric means and medians were lower than 1 μg/m³ for 83 VOCs. INTERPRETATION: The high percentage of mobile examination centre participants who deployed samplers in their homes, the sampler return rate, and the quality of the data obtained demonstrate the feasibility of relying on respondents to handle indoor air samplers for large-scale collection of residential VOC data.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".