An impactor-based aerosol platform for probing indoor, short-range transmission dynamics: a Phi6 bacteriophage study
Bibliographic record
Abstract
1. ABSTRACT Short-range transmission is a driver of airborne disease spread. However, limited knowledge exists on the immediate impact of host, environmental, and seasonal factors on viable pathogen-laden droplets (VPLD) shortly after release. This work modelled the effects of respiratory droplet size, airway mucus composition, viral loads, and seasonal variations in indoor relative humidity (RH) on the number of VPLD collected shortly after equilibration. Clinically relevant concentrations of the SARS-CoV-2 surrogate bacteriophage, Phi6, were prepared in solutions reflecting the solute content of the airway mucus in healthy and disease states. Low solute levels and viral loads, characteristic of the presymptomatic phase, increased VPLD counts (initial diameter range, 5.59 to 20.28 µm) under low indoor RH in temperate winter. Elevated solute levels and decreased viral loads, characteristic of the late symptomatic phase, reduced VPLD counts (initial diameter range, 1.73 to 20.28 µm) under intermediate RH in temperate summers. High viral loads, characteristic of the early symptomatic phase, resulted in comparable VPLD counts (initial diameter range, 1.73 to 20.28 µm) across solute levels and RH, indicating a buffering effect of high viral loads against inactivation. These findings, integrated with complementary lines of evidence, were used to describe potential mechanisms of short-range transmission dynamics. 3. SYNOPSIS This study investigated the effects of four factors including respiratory droplet size, mucus composition, viral load, and seasonal variations in indoor relative humidity, on the recovery of viable pathogen-laden droplets to understand short-range transmission dynamics.
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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 | 0.000 | 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) | 0.001 | 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".