Evaluation and Determinants of Airborne Bacterial Concentrations in School Classrooms
Bibliographic record
Abstract
A survey of 39 elementary schools was undertaken to determine indoor air concentrations of bioaerosols within a coastal, temperate climatic zone in British Columbia, Canada. This article reports the results for airborne bacteria. Determinants of exposure were grouped into environmental (outdoor temperature, relative humidity, season, weather), ventilation and comfort parameter (indoor relative humidity, temperature, indoor CO2 concentration, indoor fungal concentration), and occupancy (number of occupants, activity levels, occupancy patterns) variables. Regression modeling was used to evaluate the association between these factors and measured concentrations of indoor mesophilic bacteria. Naturally ventilated rooms (47% of the total rooms surveyed) had higher bacterial counts than mechanically ventilated rooms (geometric mean 325 vs. 166 CFU/m3, respectively, p < 0.001). In univariate analyses, bacterial counts were negatively correlated with supply and exhaust flow rates, air exchange rates, and the percentage of the day that occupants spent quietly sitting at their desks. Analysis of bacterial groups indicated various sources of the bacterial aerosol, with micrococci and staphylococci closely associated with occupancy variables, Bacillus with site and occupancy, and coryneform bacteria with site variables only. Approximately 60% of the variance in the outcome measurement of total bacteria was accountedfor by indoor CO2, lower air exchange rate, the age of the building, signs of current or old moisture stains, room volume, indoor relative humidity, and occupant activity level in a multiple linear regression model, with ventilation factors accounting for 40% of the variance.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".