Detection of Infectious <i>Cryptosporidium</i> in Filtered Drinking Water
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Monitoring of 82 surface water treatment plants revealed that 1.4% of 1,690 100‐L finished water samples tested positive for infectious Cryptosporidium using the cell culture‐polymerase chain reaction). Infectious oocysts were detected in finished water samples from 22 water treatment plants (26.8%). Genotype analysis identified 23 isolates as Cryptosporidium parvum and one isolate as Cryptosporidium hominis . All isolates subgenotyped were shown to be genetically distinct environmental isolates. Analysis of the water quality and treatment plant characteristics showed no differences between the positive and negative sites. More than 70% of the positive samples occurred in filtered water samples of <0.1 ntu, and 20% of the positive samples were in water of <0.05 ntu. There was no association among Cryptosporidium occurrence and source water type, microbial indicators, or plant operation and treatment. It was concluded that given sufficient testing, nearly all conventional treatment plants would be at risk for passing infectious oocysts. Based on this study, the overall risk of Cryptosporidium infection for conventionally treated drinking water was 52 infections/10,000 people/year, with an 80% credible range of 9–119 infections/10,000 people/year. It was also concluded based on these studies that conventional treatment requires an additional treatment barrier, such as ultraviolet light disinfection, to meet the US Environmental Protection Agency risk goals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it