Use of reference chemicals to determine passive uptake rates of common indoor air VOCs by collocation deployment of active and passive samplers
Bibliographic record
Abstract
Passive samplers have become more popular in their application in the measurement of airborne chemicals. For volatile organic compounds, the rate of a chemical's diffusivity is a determining factor in the quantity of the chemical being collected for a given passive sampler. While uptake rate of a chemical in the passive sampler can be determined either by collocation deployment of both active and passive samplers or use of controlled facilities such as environmental chambers, a new approach without a need for accurate active flow rate in the collocation experiment was demonstrated in this study. This approach uses chemicals of known uptake rates as references to calculate the actual flow rate of the active sampling in the collocation experiment. The active sampling rate in turn can be used in the determination of the uptake rates of all other chemicals present in the passive samplers. The advantage of such approach is the elimination of the errors in actual active sampling rate associated with low flow employed in the collocation experiment. Using this approach, passive uptake rates of more than 80 volatile organic compounds commonly present in indoor air were determined. These experimentally determined uptake rates correlate well with air diffusivity of the chemicals, indicating the regression equation describing such correlation might be useful in predicting the uptake rates of other volatile organic chemicals in indoor air based on their air diffusivity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".