Examination of discrete and counfounding effects of water quality parameters during the inactivation of MS2 phages and <i>Bacillus subtilis</i> pores with free chlorine
Bibliographic record
Abstract
The role of water quality (pH, temperature, turbidity, and natural organic matter (NOM)) on the efficacy of chlorine to inactivate Bacillus subtilis spores and MS2 phages was investigated in synthetic waters. A half-factorial statistical experimental design was employed to reduce the number of assays. Temperature and pH were found to be by far the most important variables in explaining chlorine efficacy, that is 78% of the variability in the experimental domain studied (pH, 6.58.5; temperature, 722 °C; dissolved organic carbon (DOC), 05 mg/L, 05 nephelometric turbidity units (NTUs)). The interaction between pH and temperature was also significant, being the third most important factor, explaining 11% of the variability. For both MS2 phages and B. subtilis spores, hypochlorous acid efficacy was less sensitive than hypochlorite ions to a modification of water temperature. Such effect is not taken into account in the current United States Environmental Protection Agency (USEPA) concentrationtime (Ct) tables. Once the consumption of disinfectant by natural organic matter (NOM) was taken into account, the presence of NOM had either no impact on MS2 phage inactivation or significantly improved B. subtilis spore inactivation. The role of inorganic turbidity was either not statistically significant or negligible. Little or no protection was therefore provided by kaolin particles, up to a concentration of 5 mg/L ([Formula: see text]5 NTU).Key words: drinking water, disinfection, spores, MS2 coliphages, chlorine, water quality, turbidity, natural organic matter.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".