Achieving safe drinking water — risk management based on experience and reality
Bibliographic record
Abstract
Over recent years there have been a number of high profile water quality incidents in the developed world that have drawn attention to the safety of our drinking water supplies and how we are managing our systems. An analysis of these and other waterborne disease outbreaks reveals some important themes about the underlying causes of outbreak failures and some broader issues about the role of drinking water quality monitoring for the protection of public health. Experience has shown that waterborne disease outbreaks in affluent countries almost universally demonstrate that the outbreaks were eminently preventable and, in most circumstances, the solutions for assuring safety from the risks of drinking water are not complex and rely not so much on implementing stringent water quality standards, as on improved system management and operation. Given these themes, assuring drinking water safety requires a commitment to a comprehensive approach to risk management, one that focuses on prevention and better measures of control extending from catchment and source protection through to the consumer. There is a growing international consensus moving towards this strategy for assuring safe drinking water, which provides the prospects of making water even more safe than it currently is most places in the developed world.
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 | 0.001 | 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 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".