A Spike Cocktail Approach to Improve Microbial Performance Monitoring for Water Reuse
Bibliographic record
Abstract
Water reuse, via either centralized treatment of traditional wastewater or decentralized treatment and on-site reuse, is becoming an increasingly important element of sustainable water management. Despite advances in waterborne pathogen detection methods, low and highly variable pathogen levels limit their utility for routine evaluation of health risks in water reuse systems. Therefore, there is a need to improve our understanding of the linkage between pathogens and more readily measured process indicators during treatment. This paper describes an approach for constructing spiking experiments to relate the behavior of viral, bacterial, and protozoan pathogens with relevant process indicators. General issues are reviewed, and the spiking protocol is applied as a case study example to improve microbial performance monitoring and health risk evaluation in a water reuse system. This approach provides a foundation for the development of novel approaches to improve real or near-real time performance monitoring of water recycling systems.
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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 | 0.002 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.007 |
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; both teacher heads agree on what is shown here.
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".