Drinking-water safety – challenges for community-managed systems
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
A targeted review of documented waterborne disease outbreaks over the past decades reveals some recurring themes that should be understood by drinking-water suppliers. Evidence indicates the outbreaks are often linked to some significant change in conditions that provides a sudden challenge to a water system. Severe weather events, such as heavy rainfall or runoff from snow melt, as well as treatment process and system changes, are common risk factors for drinking-water outbreaks. Failure to recognise warning signs and complacency are important contributors to drinking water becoming unsafe. Drinking-water suppliers must focus on competence and vigilance in maintaining effective multiple barriers appropriate to the challenges facing the drinking-water system. Understanding the risk factors and failure modes of waterborne disease outbreaks is an essential component for effective management of community drinking-water supplies and ensuring the delivery of safe drinking-water to consumers.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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