A nose for trouble – the role of off-flavours in assuring safe drinking water
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 detailed review of drinking water disease outbreaks over the past 30 years in 15 affluent countries produced over 70 case studies, some involving fatalities, which revealed a number of common factors in these disasters. Some of these outbreaks involved off-flavours, either as a driver for reducing disinfection and making the system vulnerable to pathogenic contamination or as an early warning of contamination that was not responded to with sufficient urgency or efficiency to avoid a disease outbreak. The characteristics of these outbreaks are recounted and the important link they reveal between aesthetically pleasing drinking water and safety is documented. Our analysis of common features in drinking water outbreaks also supports an argument that the failure of a water utility to be concerned about aesthetic factors makes such water supplies an inherently greater health risk for their 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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