Experimental investigation of far‐field human cough airflows from healthy and influenza‐infected subjects
Bibliographic record
Abstract
Seasonal influenza epidemics have been responsible for causing increased economic expenditures and many deaths worldwide. Evidence exists to support the claim that the virus can be spread through the air, but the relative significance of airborne transmission has not been well defined. Particle image velocimetry (PIV) and hot-wire anemometry (HWA) measurements were conducted at 1 m away from the mouth of human subjects to develop a model for cough flow behavior at greater distances from the mouth than were studied previously. Biological aerosol sampling was conducted to assess the risk of exposure to airborne viruses. Throughout the investigation, 77 experiments were conducted from 58 different subjects. From these subjects, 21 presented with influenza-like illness. Of these, 12 subjects had laboratory-confirmed respiratory infections. A model was developed for the cough centerline velocity magnitude time history. The experimental results were also used to validate computational fluid dynamics (CFD) models. The peak velocity observed at the cough jet center, averaged across all trials, was 1.2 m/s, and an average jet spread angle of θ = 24° was measured, similar to that of a steady free jet. No differences were observed in the velocity or turbulence characteristics between coughs from sick, convalescent, or healthy participants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".