Controlling <i>Giardia</i> spp. and <i>Cryptosporidium</i> spp. in drinking water by microbial reduction processes
Bibliographic record
Abstract
Drinking water microbial reduction has evolved from simple, effective chlorination to control waterborne diseases such as cholera and typhoid fever to advanced systems using ozone, chlorine dioxide, ultraviolet radiation, and combinations of disinfectants to control waterborne diseases such as poliomyelitis, hepatitis, giardiasis, and cryptosporidiosis. Giardia spp. and Cryptosporidium spp. have posed a major challenge to the water industry from a variety of perspectives. They occur in low concentrations in source waters, their infective doses in humans are low when compared with typical waterborne viruses and bacteria, they are difficult to inactivate with chlorine compounds, and they are difficult to determine if they are dead when detected in the environment or after microbial reduction in water treatment. However, Giardia spp. and Cryptosporidium spp. are readily controlled by ozone or ultraviolet radiation over a wide range of water-quality conditions. Chlorine dioxide provides a simple alternative to chlorine in some circumstances. Using modern microbial reduction process design techniques such as the integrated disinfection design framework (IDDF) ensures the provision of drinking water with a low risk of transmitting human pathogens to the consumer. Key words: ozone, chlorine dioxide, chlorine, ultraviolet, disinfection, microbial reduction, drinking water, Giardia, Cryptosporidium, parasite.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".