Examination of discrete and counfounding effects of water quality parameters during the inactivation of MS2 phages and <i>Bacillus subtilis</i> spores with chlorine dioxide
Bibliographic record
Abstract
The role of water quality (pH, temperature, turbidity, and natural organic matter (NOM)) on the efficacy of chlorine dioxide to inactivate Bacillus subtilis spores and MS2 phages was investigated in synthetic waters. Modelling the curves describing tailing inactivation with a parallel Chick-Watson model proved to be a valid approach. The formation of aggregates was exacerbated when using chlorine dioxide as opposed to free chlorine. The origin of these aggregates lies in the interactions of chlorine dioxide with the water matrix and the microorganisms. Higher temperature and higher turbidity were dominant factors in predicting spores tailing, while decreasing the pH from 8.5 to 6.5 was responsible for increasing the fraction of MS2 aggregates from 0.06% to 9.0% (138-fold). The resistance of aggregates were, on average, 18–21 times higher than for single organisms. The addition of dissolved organic carbon significantly (p < 0.01) improved inactivation with chlorine dioxide. Turbidity (5 NTU) did not significantly hinder MS2 inactivation, but it increased the concentration–time (Ct) 1-log of B. subtilis spores from 386 to 600 mg·min·L –1 . Key words: drinking water, disinfection, spores, MS2 coliphages, chlorine dioxide, water quality, turbidity, natural organic matter.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".