Establishing minimum free chlorine residual concentration for microbial control in a municipal drinking water distribution system
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
Distribution system data from a Nova Scotia municipal drinking water supply was collected over four years, including free chlorine residual concentration, heterotrophic plate count (HPC) bacteria, and temperature. These data were analyzed for occurrences of HPC bacteria greater than 500 colony forming units (CFU)/mL. The municipality was interested in determining if secondary chlorination practices were sufficient in maintaining microbial health in their distribution system. Coliform data were non-detect (total coliforms and Escherichia coli) in the distribution system over this period and thus heterotrophic bacteria were used to assess microbial health. Results were compared to similar data collected from pilot-scale studies that had been carried out using the same municipal water as the source. Analysis showed that a similar trend was observed between pilot- and full-scale samples. Full-scale data analysis revealed that the minimum disinfection requirement of 0.2 mg/L did not consistently control occurrences of heterotrophic bacteria from being greater than 500 CFU/mL. By comparison, maintaining a concentration of 0.3 mg/L or above, particularly in warm-weather systems, maintained the number of heterotrophic bacteria at below 500 CFU/mL. Fortunately the majority of samples collected in the full-scale distribution system (>89%) had a free chlorine residual concentration of greater than 0.30 mg/L. While it is recognized that this system had 100% compliance for E. coli, the goal of this work will help utilities understand how to utilize microbial data to inform operational disinfection targets for their distribution system.
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 | 0.001 | 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 | 0.001 |
| 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 it