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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".