Are microbial indicators and pathogens correlated? A statistical analysis of 40 years of research
Bibliographic record
Abstract
Indicator organisms are used to assess public health risk in recreational waters, to highlight periods of challenge to drinking water treatment plants, and to determine the effectiveness of treatment and the quality of distributed water. However, many have questioned their efficacy for indicating pathogen risk. Five hundred and forty cases representing independent indicator-pathogen correlations were obtained from the literature for the period 1970-2009. The data were analyzed to assess factors affecting correlations using a logistic regression model considering indicator classes, pathogen classes, water types, pathogen sources, sample size, the number of samples with pathogens, the detection method, year of publication and statistical methods. Although no single indicator was identified as the most correlated with pathogens, coliphages, F-specific coliphages, Clostridium perfringens, fecal streptococci and total coliforms were more likely than other indicators to be correlated with pathogens. The most important factors in determining correlations between indicator-pathogen pairs were the sample size and the number of samples positive for pathogens. Pathogen sources, detection methods and other variables have little influence on correlations between indicators and pathogens. Results suggest that much of the controversy with regards to indicator and pathogen correlations is the result of studies with insufficient data for assessing correlations.
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 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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".