Evaluating ATP testing for distribution system monitoring: comparison to HPC, impact of chlorine quenching, and hold time dependency
Bibliographic record
Abstract
Adenosine triphosphate (ATP) assays have a faster turnaround time and higher sensitivity than traditional cultivation methods for microbial monitoring. Challenges implementing ATP testing include incompatibility with chlorine quenching agents and hold time sensitivity, which are not well-studied. Chlorinated distribution system samples were collected from two Canadian utilities, Metro Vancouver (n = 40 samples) and Halifax Water (n = 283). No significant correlations were observed between heterotrophic cell count (HPC) and cellular ATP, suggesting these do not correlate well in waters with low biological activity (median HPC < 2 CFU/mL). However, interpretation of HPC and cATP results (based on the HPC guideline of 100 CFU/mL and cATP of 10 pg/mL) yielded the same conclusion for 95% of samples, suggesting a potential decision-making framework to replace HPC with cATP. Moreover, cATP correlates better with free chlorine (p < 0.04) compared with HPC for one of the studied systems. Importantly, adding chlorine quench (10% sodium thiosulfate) did not produce significantly different cATP results, nor did analyzing at various hold times of 4-, 6-, and 24-h. This study supports the integration of ATP testing into existing sampling procedures for water utilities, as a sensitive, fast, and reliable monitoring method.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".