Water Quality Evaluation of Groundwater and Dam Reservoir Water: Application of the Water Quality Index to Study Sites in Greece
Bibliographic record
Abstract
To detect the degradation of clean water, it is necessary to characterize its quality through water quality indices using seasonal water sampling and analysis. In the present study, the initialization of the monitoring by surface and dam water sampling was conducted in multiple areas of Greece, including the Eastern Thermaikos Gulf, Mouriki, and Marathonas basins, during both the dry and wet periods of 2022. The dam reservoirs were also monitored by capturing their orthomosaic mapping. The classification of the samples according to the Canadian Council of Ministers of Environment Water Quality Index (CCME WQI) showed that all dam water samples examined and Mouriki area samples have excellent water quality in terms of physical and chemical characteristics. However, some samples from the Eastern Thermaikos Gulf and Marathonas basins suffer from seawater intrusion, which is indicated by the high concentration levels of Na+ and Cl−, and anthropogenic activities shown by the elevated concentrations of NO3−. Moreover, the high concentration of As in samples from the Eastern Thermaikos Gulf is attributed to geothermal fluids. The importance of Cl−, NO3−, and As presence in water quality at the studied areas is also verified by the sensitivity analysis performed, pointing out the requirement of sustainable management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".