A critical review of risk matrices used in water safety planning: improving risk matrix construction
Bibliographic record
Abstract
Risk matrices are used in water safety planning to prioritize improvements to drinking water systems. While water safety plans (WSPs) are promoted globally, no study has evaluated whether risk matrices are comprehensively constructed to accurately assess risk. We used risk matrix criteria adapted from previous risk matrix research to evaluate risk matrices found in twelve templates across global jurisdictions. WSP templates were found using the WSPortal website and definitions of likelihood and impact were extracted from each template to assist in the evaluation of WSP risk matrices. Application of the criteria developed from a detailed mathematical analysis by revealed that 11 of 12 risk matrices evaluated contravene at least one of the risk matrix criteria. Furthermore, definitions of likelihood and impact varied widely across different jurisdictions, due in part to the system specific nature of the WSP methodology. To improve risk matrix construction, we recommend: setting clearer risk level boundary criteria, aligning specific impact category definitions with water system objectives, and selecting specific impact categories as opposed to defining impact in several ways. Finally, we recommend risk matrix construction be reviewed as part of the WSP process to ensure accurate identification of key risks in a water system.
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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 | 0.025 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".