Improved Detection of F-Specific RNA Coliphages in Fecal Material by Extraction and Polyethylene Glycol Precipitation
Bibliographic record
Abstract
Male-specific RNA coliphages (F-RNA coliphages) have been proposed as a potential viral indicator of fecal contamination in water and foods because they are easy to culture and are a normal component of the mammalian gut flora. F-RNA coliphage plaque numbers are typically obtained by directly plating a 10-fold dilution of 1 g of fecal material, but the numbers of F-RNA coliphages shed by animals and humans may be too low for direct enumeration. Therefore, the sensitivity of detecting F-RNA coliphages in fecal material was improved by extracting and precipitating F-RNA coliphage from a 10-g fecal sample by use of polyethylene glycol (PEG). The highest recovery of F-RNA coliphage with 10% beef extract, pH 7.2, was obtained in the presence of 1 M NaCl and 10% PEG after 16 h of precipitation, but a pellet was not obtained after a short precipitation time of 2 h. There was no significant difference between eluant-to-fecal-material ratios of 4:1 and 9:1 or homogenization with a stomacher or pulsifier. F-RNA coliphage were detected in 64% (16 of 25 samples) of fecal samples from various sources when the sample size was 10 g but in 36% (9 of 25 samples) of samples when the sample size was 1 g. When F-RNA coliphage were detected in 1-g samples, they were also detected in 10-g samples. When F-RNA coliphage were detected in 10-g samples but not in 1-g samples, the levels were <100 PFU/g.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".