MANAGING THE MICROBIOLOGICAL RISKS OF DRINKING WATER
Bibliographic record
Abstract
The microbiological contamination of drinking water supplies can have serious health consequences for consumers, and this has been dramatically illustrated in recent years by two disease outbreaks in Canada. In this paper, some factors that can influence the microbiological quality of drinking water and its management are examined. Frameworks have been proposed that help to clarify the main elements of health risk assessment and risk management, and, in accordance with these, risks can be logically characterized, evaluated and controlled. A protocol has been developed for microbiological risk assessment and a risk management framework now guides the development of Canada's national guidelines for drinking-water quality. Monitoring of indicator organisms and the application of adequate water treatment are the primary means recommended in the Canadian guidelines to safeguard health from the presence of water-borne pathogens. Understanding the biological characteristics of microbial pathogens is necessary for assessing their impact on community health and appraising the rationale behind drinking-water testing methods and their limitations. Improvements in health surveillance, monitoring, and risk characterization and application of concepts such as multiple barriers (source-to-tap) and total quality management should contribute to better management of the microbiological quality of drinking water.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".