Using turbidity and particle counts to monitor Cryptosporidium removals by filters
Bibliographic record
Abstract
Physicochemical removal of protozoan pathogens is receiving increased attention because of the difficulty of chemically inactivating these organisms, particularly Cryptosporidium parvum. Most studies that have examined the removal of these and other pathogens by filtration have been conducted under steady-state conditions with optimized pre-treatment. The research on which this paper is based evaluated the removal of Cryptosporidium and surrogates at various points in the filter cycle and under non-optimal conditions, at several pilot plants. The focus of this paper is on the relationship between removals of Cryptosporidium, and turbidity and particle counts. Under stable or optimal operating conditions all pilot plants produced similar low filter effluent turbidity and particle counts. Average Cryptosporidium removal varied among locations, however, by more than two log units. Cryptosporidium removal was impaired under all of the non-optimal conditions. Particle and turbidity performance was also worse, but the relationship of these parameters to Cryptosporidium removal varied. Particle counts show greater promise than turbidity as a tool to monitor filter performance in real time for possible deterioration in Cryptosporidium removal capability.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".