Estimating droplet size and count distributions over a prolonged period of time following a cough in indoor environments
Bibliographic record
Abstract
An empirical correlation and a set of machine learning (ML) models were developed to estimate droplet size and count distributions over an extended duration after a cough at different relative humidities (RHs), air temperatures and locations within an indoor environment. Experiments covered RHs of 20%–80% and air temperatures of 21 °C–26 °C. Droplet count distributions for 4 size bins (0.3–0.5, 0.5–1, 1–3 and 3–5 μm) were recorded for 70 min within the distance of 2 m from the cough source. Different ML models, including decision tree, random forest and artificial neural network, were trained for each size bin to predict the associated count distribution. Amongst these models, random forest showed a slight superiority in performance. The coefficient of determination for the random forest models ranged from 0.912 to 0.989, indicating robust correlations between the features and the response variables. An empirical correlation was established linking the count distribution of 0.3–0.5 μm droplets to time, RH and distance along the cough direction. Both ML models and the correlation accurately predicted the trends and the distributions, providing valuable data for validating computational simulations and informing indoor environment control systems to reduce the risk of virus transmission.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".