Batch test to evaluate microbial disinfectant decay and the onset of nitrification
Bibliographic record
Abstract
Abstract A batch test procedure was investigated to provide insight into the microbial contribution to disinfectant decay in drinking water distribution systems using chloramines. A modified method for determining the critical threshold residual (CTR), the intersection point on a semi-log plot between first-order total chlorine fitted decay curves before and after the breakpoint, was developed. Unlike the CTR as originally defined, initial sample conditions were retained rather than artificially raising the monochloramine concentrations. The CTR calculated with this modified method can more easily be applied to distribution system scenarios. In addition, four types of decay curves were identified and could distinguish differences in the microbial contribution to disinfectant residual decay. This study revealed that chloramine decay batch tests should be evaluated based on decay curve type, decay rates, and the CTR value, in addition to the microbial decay factor, which has been used alone in previous studies. The batch test approach and evaluation criteria established here can be used to predict conditions favorable for rapid chloramine decay and nitrification, and that monitoring and control strategies should be implemented.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".