Calculating Required Purification Effort to Turn Source Water into Drinking Water Using an Adapted CCME Water Quality Index
Bibliographic record
Abstract
The 2000 European Union Water Framework Directive (WFD) states that ‘Member States shall ensure the necessary protection for the bodies of water identified with the aim of avoiding deterioration in their quality in order to reduce the level of purification treatment required in the production of drinking water’. However, it does not specify how to evaluate or quantify this level of purification treatment. The scientific literature contains several different Water Quality Indices (WQIs), but none are suited for this purpose. Therefore, we propose a novel WQI that we specifically designed to quantify the level of purification required to prepare drinking water from source water. It is based on the WQI of the Canadian Council of Ministers of the Environment (CCME WQI), which was chosen because it is widely accepted, can be used with any number of input parameters, does not require expert judgement and has been applied to assess source water quality before. We compare measured contaminant concentrations in source water to drinking water guidelines and additionally incorporate the resilience of contaminants to treatment processes in the index (which is not possible in the CCME WQI). Furthermore, we accommodate for varying sampling frequencies that are characteristic of the ongoing monitoring programme. These changes make our index more robust and sensitive to relevant changes in source water quality. We calculated index scores for source water from the Rhine and the Meuse rivers to monitor the effect of implementation of the WFD on the effort required to produce of drinking water.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".