Evaluation and a Predictive Model of Airborne Fungal Concentrations in School Classrooms
Bibliographic record
Abstract
Exposure to airborne fungal products may be associated with health effects ranging from non-specific irritation of the respiratory tract or mucus membranes to inflammation provoked by specific fungal antigens. While concentrations of airborne fungi are frequently measured in indoor air quality investigations, the significance of these measurements in the absence of visual mold colonization is unclear. This study was undertaken to evaluate concentrations of airborne fungal concentrations in school classrooms within a defined geographic location in British Columbia, Canada, and to build a model to clarify determinants of airborne fungal concentration. All elementary schools within one school district participated in the study. Classrooms examined varied by age, construction and presence or absence of mechanical ventilation. Airborne fungal propagules were collected inside classrooms and outdoors. Variables describing characteristics of the environment, buildings and occupants were measured and used to construct a predictive model of fungal concentration. The classrooms studied were not visibly contaminated by fungal growth. The data were evaluated using available guidelines. However, the published guidelines did not take into account significant aspects of the local environment. For example, there was a statistically significant effect of season on the fungal concentrations and on the proportional representation of fungal genera. Rooms ventilated by mechanical means had significantly lower geometric mean concentrations than naturally ventilated rooms. Environmental (temperature, outdoor fungal concentration), building (age) and ventilation variables accounted for 58% of the variation in the measured fungal concentrations. A methodology is proposed for the evaluation of airborne fungal concentration data which takes into account local environmental conditions as an aid in the evaluation of fungal bioaerosols in public buildings.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".