Bacteriophages as indicators of faecal pollution and enteric virus removal
Bibliographic record
Abstract
Bacteriophages are an attractive alternative to faecal indicator bacteria (FIB), particularly as surrogates of enteric virus fate and transport, due to their closer morphological and biological properties. Based on a review of published data, we summarize densities of coliphages (F+ and somatic), Bacteroides spp. and enterococci bacteriophages (phages) in individual human waste, raw wastewater, ambient fresh and marine waters and removal through wastewater treatment processes utilizing traditional treatments. We also provide comparisons with FIB and enteric viruses whenever possible. Lastly, we examine fate and transport characteristics in the aquatic environment and provide an overview of the environmental factors affecting their survival. In summary, concentrations of bacteriophages in various sources were consistently lower than FIB, but more reflective of infectious enteric virus levels. Overall, our investigation indicates that bacteriophages may be adequate viral surrogates, especially in built systems, such as wastewater treatment plants. SIGNIFICANCE AND IMPACT OF THE STUDY: Bacteriophage are alternative fecal indicators that may be better surrogates for viral pathogens than fecal indicator bacteria (FIB). This report offers a summary of the existing literature concerning the utility of bacteriophage as indicators of viral presence (fecal sources and surface waters) and persistence (in built infrastructure and aquatic environments). Our findings indicate that bacteriophage levels in all matrices examined are consistently lower than FIB, but similar to viral pathogens. Furthermore, in built infrastructure (e.g. wastewater treatment systems) bacteriophage closely mimic viral pathogen persistence suggesting they may be adequate sentinels of enteric virus removal.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".