Airborne Infectious Agents and Other Pollutants in Automobiles for Domestic Use: Potential Health Impacts and Approaches to Risk Mitigation
Bibliographic record
Abstract
The world total of passenger cars is expected to go from the current one billion to >2.5 billion by 2050. Cars for domestic use account for ~74% of the world's yearly production of motorized vehicles. In North America, ~80% of the commuters use their own car with another 5.6% travelling as passengers. With the current life-expectancy of 78.6 years, the average North American spends 4.3 years driving a car! This equates to driving 101 minutes/day with a lifetime driving distance of nearly 1.3 million km inside the confined and often shared space of the car with exposure to a mix of potentially harmful pathogens, allergens, endotoxins, particulates, and volatile organics. Such risks may increase in proportion to the unprecedented upsurge in the numbers of family cars globally. Though new technologies may reduce the levels of air pollution from car exhausts and other sources, they are unlikely to impact our in-car exposure to pathogens. Can commercial in-car air decontamination devices reduce the risk from airborne infections and other pollutants? We lack scientifically rigorous protocols to verify the claims of such devices. Here we discuss the essentials of a customized aerobiology facility and test protocols to assess such devices under field-relevant conditions.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".