Impact of stagnation and sampling volume on water microbial quality monitoring in large buildings
Bibliographic record
Abstract
Microbial drinking water quality can be altered in large buildings, especially after stagnation. In this study, bacterial profiles were generated according to the stagnation time and the volume of water collected at the tap. Successive volumes of cold and hot water were sampled after controlled stagnation periods. Bacterial profiles revealed an important decline (> 2 log) in culturable cells in the first 500 mL sampled from the hot and cold water systems, with a steep decline in the first 15 mL. The strong exponential correlation (R2 ≥ 0.97) between the culturable cell counts in water and the pipe surface-to-volume ratio suggests the biofilm as the main contributor to the rapid increase in suspended culturable cells measured after a short stagnation of one-hour. Results evidence the contribution of the high surface-to-volume ratio at the point of use and the impact of short stagnation times on the increased bacterial load observed. Simple faucets with minimal internal surface area should be preferred to minimize surface area. Sampling protocol, including sampling volume and prior stagnation, was also shown to impact the resulting culturable cell concentration by more than 1000-fold. Sampling a smaller volume on first draw after stagnation will help maximize recovery of bacteria.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".