Drinking water quality management: a holistic approach
Bibliographic record
Abstract
A growing list of water contaminants has led to some water suppliers relying primarily on compliance monitoring as a mechanism for managing drinking water quality. While such monitoring is a necessary part of drinking water quality management, experiences with waterborne disease threats and outbreaks have shown that compliance monitoring for numerical limits is not, in itself, sufficient to guarantee the safety and quality of drinking water supplies. To address these issues, the Australian National Health and Medical Research Council (NHMRC) has developed a Framework for Management of Drinking Water Quality (the Framework) for incorporation in the Australian Drinking Water Guidelines, the primary reference on drinking water quality in Australia. The Framework was developed specifically for drinking water supplies and provides a comprehensive and preventive risk management approach from catchment to consumer. It includes holistic guidance on a range of issues considered good practice for system management. The Framework addresses four key areas: Commitment to Drinking Water Quality Management, System Analysis and System Management, Supporting Requirements, and Review. The Framework represents a significantly enhanced approach to the management and regulation of drinking water quality and offers a flexible and proactive means of optimising drinking water quality and protecting public health. Rather than the primary reliance on compliance monitoring, the Framework emphasises prevention, the importance of risk assessment, maintaining the integrity of water supply systems and application of multiple barriers to assure protection of public health. Development of the Framework was undertaken in collaboration with the water industry, regulators and other stakeholder, and will promote a common and unified approach to drinking water quality management throughout Australia. The Framework has attracted international interest.
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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".