Alternative Fecal Indicators and Their Empirical Relationships with Enteric Viruses, Salmonella enterica, and Pseudomonas aeruginosa in Surface Waters of a Tropical Urban Catchment
Bibliographic record
Abstract
The suitability of traditional microbial indicators (i.e., Escherichia coli and enterococci) has been challenged due to the lack of correlation with pathogens and evidence of possible regrowth in the natural environment. In this study, the relationships between alternative microbial indicators of potential human fecal contamination (Bacteroides thetaiotaomicron, Methanobrevibacter smithii, human polyomaviruses [HPyVs], and F+ and somatic coliphages) and pathogens (Salmonella spp., Pseudomonas aeruginosa, rotavirus, astrovirus, norovirus GI, norovirus GII, and adenovirus) were compared with those of traditional microbial indicators, as well as environmental parameters (temperature, conductivity, salinity, pH, dissolved oxygen, total organic carbon, total suspended solids, turbidity, total nitrogen, and total phosphorus). Water samples were collected from surface waters of urban catchments in Singapore. Salmonella and P. aeruginosa had significant positive correlations with most of the microbial indicators, especially E. coli and enterococci. Norovirus GII showed moderately strong positive correlations with most of the microbial indicators, except for HPyVs and coliphages. In general, high geometric means and significant correlations between human-specific markers and pathogens suggest the possibility of sewage contamination in some areas. The simultaneous detection of human-specific markers (i.e., B. thetaiotaomicron, M. smithii, and HPyVs) with E. coli and enterococcus supports the likelihood of recent fecal contamination, since the human-specific markers are unable to regrow in natural surface waters. Multiple-linear-regression results further confirm that the inclusion of M. smithii and HPyVs, together with traditional indicators, would better predict the occurrence of pathogens. Further study is needed to determine the applicability of such models to different geographical locations and environmental conditions.
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.
How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".