Airflow Dynamics of Human Jets: Sneezing and Breathing - Potential Sources of Infectious Aerosols
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Natural human exhalation flows such as coughing, sneezing and breathing can be considered as 'jet-like' airflows in the sense that they are produced from a single source in a single exhalation effort, with a relatively symmetrical, conical geometry. Although coughing and sneezing have garnered much attention as potential, explosive sources of infectious aerosols, these are relatively rare events during daily life, whereas breathing is necessary for life and is performed continuously. Real-time shadowgraph imaging was used to visualise and capture high-speed images of healthy volunteers sneezing and breathing (through the nose - nasally, and through the mouth - orally). Six volunteers, who were able to respond to the pepper sneeze stimulus, were recruited for the sneezing experiments (2 women: 27.5±6.36 years; 4 men: 29.25±10.53 years). The maximum visible distance over which the sneeze plumes (or puffs) travelled was 0.6 m, the maximum sneeze velocity derived from these measured distances was 4.5 m/s. The maximum 2-dimensional (2-D) area of dissemination of these sneezes was 0.2 m(2). The corresponding derived parameter, the maximum 2-D area expansion rate of these sneezes was 2 m(2)/s. For nasal breathing, the maximum propagation distance and derived velocity were 0.6 m and 1.4 m/s, respectively. The maximum 2-D area of dissemination and derived expansion rate were 0.11 m(2) and 0.16 m(2)/s, respectively. Similarly, for mouth breathing, the maximum propagation distance and derived velocity were 0.8 m and 1.3 m/s, respectively. The maximum 2-D area of dissemination and derived expansion rate were 0.18 m(2) and 0.17 m(2)/s, respectively. Surprisingly, a comparison of the maximum exit velocities of sneezing reported here with those obtained from coughing (published previously) demonstrated that they are relatively similar, and not extremely high. This is in contrast with some earlier estimates of sneeze velocities, and some reasons for this difference are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it