Filter operation effects on plant‐scale microbial risk: Opportunities for enhanced treatment performance
Bibliographic record
Abstract
Abstract Granular media filtration remains a critical treatment process and regulatory requirement for managing pathogenic protozoa in drinking water. It is a dynamic process in which performance inherently varies. While research has focused on characterizing or maximizing (oo)cyst removal in individual filters, the risk implications of combinations of filters moving through different phases of the filter cycle (leading to temporal variation in plant‐scale performance) have not been described. Increasing threats from climate‐change‐exacerbated landscape disturbances leading to more variable source water quality emphasize the need for such evaluations. Here, a modeling framework was developed to investigate the impacts of individual filter performance variation on plant‐scale performance. It is shown that improving maximal removal during stable operation does not necessarily improve average performance. The effect of other design and operational strategies like increasing the number of filters or implementing proactive operations (e.g., avoiding breakthrough) are analyzed, thereby providing guidance for increasing treatment resilience.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".