Implementing the CCME Water Quality Index for the Evaluation of the Physicochemical Quality of Greek Rivers
Bibliographic record
Abstract
Water quality indices (WQIs) are efficient tools, globally used for the determination of the quality status of water bodies. In Greece, for almost a decade, the physicochemical quality of water in rivers has been determined by a rigorous, biologically-based, national classification system, developed by the Hellenic Centre for Marine Research (HCMR), through the calculation of a simple water quality index (HWQI) that takes into account six water parameters: five nutrient species and dissolved oxygen. Taking the HWQI as a reference, the present study attempts to implement the Canadian Council of Ministers of Environment Water Quality Index (CCME WQI), which is globally applied and flexible in the number of parameters used, to investigate its possible suitability for Greek rivers, which are characterized by a variety of climatic, geologic, and hydrological conditions and have experienced anthropogenic impact. A large dataset consisting of 111 river sites and multiple sampling campaigns for each site in 2018–2020 were used in the analysis, giving rise to a representative application of the CCME WQI on a national scale. Furthermore, the physicochemical quality results were compared with those derived by the HWQI. Apart from the original equation of the CCME WQI for calculating the classification score, a modified version from the literature was used as well. Moreover, apart from the six conventional parameters, which offered a direct comparison with the output values of the HWQI, the CCME WQI and its modified version were recalculated based on a larger dataset, including four additional physicochemical water parameters. The comparative results from all calculations revealed the conservative behavior of the CCME WQI and confirmed the indications from several other Greek studies. Estimated water quality represented a status that consistently belonged to at least a two-class inferior category than the HWQI, while adequate reductions in this deviation could not be achieved with the modified index or with the increase in the number of parameters used in the analysis. It is thus concluded that the first calculation factor and the class boundaries of the CCME WQI are the limiting factors for successful implementation in Greek rivers, independent of the hydroclimatic, geomorphological, and anthropogenic impact variability across the country.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".