Bacterial and fungal ecology on air conditioning cooling coils is influenced by climate and building factors
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
The presence of biofilms on the cooling coils of commercial air conditioning (AC) units can significantly reduce the heat transfer efficiency of the coils and may lead to the aerosolization of microbes into occupied spaces of a building. We investigated how climate and AC operation influence the ecology of microbial communities on AC coils. Forty large-scale commercial ACs were considered with representation from warm-humid and hot-dry climates. Both bacterial and fungal ecologies, including richness and taxa, on the cooling coil surfaces were significantly impacted by outdoor climate, through differences in dew point that result in increased moisture (condensate) on coils, and by the minimum efficiency reporting value (MERV 8 vs MERV 14) of building air filters. Based on targeted qPCR and sequence analysis, low efficiency upstream filters (MERV 8) were associated with a greater abundance of pathogenic bacteria and medically relevant fungi. As the implementation of air conditioning continues to grow worldwide, better understanding of the factors impacting microbial growth and ecology on cooling coils should enable more rational approaches for biofilm control and ultimately result in reduced energy consumption and healthier buildings.
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) | 0.001 | 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 it