Comparison of static and dynamic disinfection models for bacteria and viruses in water of varying quality
Bibliographic record
Abstract
Disinfection studies rarely use natural waters due to demands exerted on the applied disinfectants and lack of consistent disinfectant residuals. This study compared the degree of disinfection achieved in natural waters between conventional batch (static) models and a system of similar volume where disinfectant residuals were maintained at constant levels (dynamic). In the latter, disinfectant was delivered through a hollow fibre cartridge from a slipstream of a full-scale (chloramine) or pilot (chlorine) water treatment plant. The test organisms (hepatitis A virus, poliovirus, MS-2, Mycobacterium terrae and Enterococcus durans) were selected with different resistance to the disinfectants. In general, for water of "good" quality, the differences between the two systems were often small or not apparent for monochloramine. However, for low chlorine residuals, or when additional demand was placed on the disinfectant, differences between the two systems became more apparent. Little difference was seen between disinfection of the test organisms singly or in mixtures, but injury of vegetative bacteria with monochloramine was very apparent. This system could be useful for understanding the fluctuations in disinfection efficacy that may occur in source water of varying quality, or in distribution systems, as disinfectant residuals decline.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".