Pathogens in Water: Value and Limits of Correlation with Microbial Indicators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article discusses the value and limitations of using microbial indicators to predict occurrence of enteric pathogens in water. Raw or treated sewage is a primary source of fecal contamination of the receiving surface water or groundwater; hence, understanding the relationship between pathogens and indicators in sewage is an important step in understanding the correlation in receiving waters. This article presents three different datasets representing different concentrations of pathogens and microbial indicators: sewage containing high concentrations of pathogens and indicators, surface water with variable concentrations, and groundwater with low concentrations. In sewage, even with very high levels of microorganisms, no mathematical correlation can predict the type or concentration of any pathogen. After discharge in the environment, direct correlation becomes biologically improbable as dilution, transport, and different inactivation rates occur in various environments. In surface waters, advanced statistical methods such as logistic regression have provided some level of predictability of the occurrence of pathogens but not specific counts. In groundwater, the continuous absence of indicators indicates an improbable occurrence of pathogen. In contrast, when these indicators are detected, pathogen occurrence probability increases significantly. In groundwater, given the nature and dissemination pattern of pathogenic microorganisms, a direct correlation with fecal microbial indicators is not observed and should not be expected. However, the indicators are still useful as a measure of risk. In summary, many pathogens of public health importance do not behave like fecal microbial indicators, and there is still no absolute indicator of their presence, only a probability of their co-occurrence.
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.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it