Improving the measurement of air change rates using the decay method: Quantifying the uncertainty of the well-mixed assumption and identifying the required sampling locations
Bibliographic record
Abstract
Improving building ventilation has emerged as a vital imperative in today’s post-Covid-19 era, while accurately estimating air change rates continues to present a considerable challenge. For more than fifty years, the decay method has been employed for this purpose, assuming the well-mixed condition that rarely occurs. However, existing mixing models (e.g., K or E z ) are limited in addressing this gap since their reported data are subjective and inconsistent across different standards (ASHRAE and AIHA). Therefore, we developed a novel modified decay method that includes two proposed factors: the uniformity index ( U i ) and the sampling factor ( S f ). These two factors help to quantify the well-mixed assumption’s uncertainty and identify the minimum required sampling locations for tracer measurements. The modified decay method is tested in a classroom, measuring the spatial variations of CO 2 using automated data acquisition. The proposed method significantly reduced the error caused by the well-mixing assumption of estimated air change rates from 26% to 3%. The sampling locations are identified as a function of the zone’s geometry. The findings of this study can be used to improve the ventilation performance of buildings.
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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 | 0.006 |
| 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 | 0.001 |
| 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".