Building and environmental factors that influence bacterial and fungal loading on air conditioning cooling coils
Bibliographic record
Abstract
We investigated bacterial and fungal concentrations on cooling coils of commercial AC units and quantified associations between microbial loads and AC unit or building operational parameters. A field campaign was conducted to sample 25 AC units in the humid, subtropical climate of Southern CT, USA and 15 AC units in the hot-summer Mediterranean climate of Sacramento, CA, USA. Median concentrations (with interquartile range) of bacteria and fungi on the cooling coils were 1.2 × 107 (5.1 × 106-3.9 × 107) cells/m2 and 7.6 × 105 (5.6 × 104-4.4 × 106) spore equivalents (SE)/m2, respectively. Concentrations varied among units with median unit concentrations ranging three orders of magnitude for bacteria and seven orders of magnitude for fungi. Controlled comparisons and multivariable regressions indicate that dominant factors associated with AC coil loading include the nominal efficiency of upstream filters (P = .008 for bacteria and P < .001 for fungi) and coil moisture, which was reflected in fungal loading differences between top and bottom halves of the AC coils in Southern CT (P = .05) and the dew points of the two climates considered (P = .04). Environmental and building characteristics explained 42% (P < .001) of bacterial concentration variability and 66% (P < .001) of fungal concentration variability among samples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".