A practical method and its applications to prioritize volatile organic compounds emitted from building materials based on ventilation rate requirements and ozone-initiated reactions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Volatile organic compounds emissions from building materials can be a major pollution source in low-occupant-density spaces. Composite-style indoor air quality references, which reflect the combined effects of multiple volatile organic compounds, can be used to determine ventilation rate requirements based on building material emissions. The lowest concentration of interest concept was adopted to implement the idea. Twenty-eight building materials selected from the National Research Council of Canada database were subjected to emission modelling, resulting in 101 volatile organic compounds as a starting volatile organic compound pool. A method was proposed to generate a volatile organic compound priority list that determines ventilation rate requirements while considering ozone-initiated reactions. Three priority lists were obtained based on three lowest concentration of interest schemes, i.e., AFSSET, AgBB and EU-LCI, with each consisting of 17–21 volatile organic compounds that were most likely to attribute to large ventilation rate requirements. Also, analyses of selected volatile organic compounds showed that the changes in the composition of the priority lists due to ozone-initiated reactions could be ignored at a typical indoor ozone concentration level. The application of priority lists was discussed for source control and air cleaning device testing. This paper provides a method to prioritize the chemicals based on ventilation rate requirements with a goal of developing volatile organic compound control strategies at building design stage.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it