Detection of <i>Bacteroidales</i> Fecal Indicators and the Zoonotic Pathogens <i>E. coli</i> O157:H7, <i>Salmonella</i>, and <i>Campylobacter</i> in River Water
Bibliographic record
Abstract
Bacteroidales host-specific PCR offers a rapid method of diagnosing fecal pollution in water and identifying sources of input. To assess human health risks from exposure to fecal pathogens, however, Bacteroidales markers should be detectable when pathogens are present. To determine if Bacteroidales general, human-, ruminant-, and swine-specific markers correlate with certain fecal pathogens, we conducted a retrospective study on water samples for which the presence of E. coli O157:H7, Salmonella spp., and Campylobacter spp. had been determined. We found a positive relationship between detection of the Bacteroidales general fecal marker and presence of the pathogens. Detection of ruminant-specific markers predicted E. coli O157: H7 occurrence. There was a significant increase in the likelihood of detecting Salmonella when a ruminant marker was present, and Campylobacter spp. when human markers were present. For pathogens such as E. coli O157: H7 that are strongly associated with particular hosts, Bacteroidales host-specific markers can estimate the likelihood of pathogen occurrence, enabling more accurate health risk assessments.
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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 | 0.001 |
| 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) | 0.001 | 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".