Defining Risk-Based Monitoring Frequencies to Verify the Performance of Water Treatment Barriers
Bibliographic record
Abstract
Preventing failures of water treatment barriers can play an important role in meeting the increasing demand for microbiologically safe water. The development and integration of failure prevention strategies into quantitative microbial risk assessment (QMRA) offer opportunities to support the design and operation of treatment trains. This study presents existing failure models and extends them to guide the development of risk-based operational monitoring strategies. For barriers with rapid performance loss, results show that a failure of 15 s should be reliably detected to verify a log reduction value (LRV) of 6.0; thus, detecting and remediating these failures may be beyond current technology. For chemical disinfection with a residual, failure durations in order of minutes should be reliably detected to verify a LRV of 6.0. Short-term failures are buffered because the disinfectant residual concentration sustains a partial reduction performance. Therefore, increasing the contact time and hydraulic mixing reduces the impact of failures. These findings demonstrate the importance of defining precise frequencies to monitor barrier performances during operation. Overall, this study highlights the utility of process-specific models for developing failure prevention strategies for water safety management.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".