Applying Factor Analysis and the CCME Water Quality Index for Assessing Groundwater Quality of an Aegean Island (Rhodes, Greece)
Bibliographic record
Abstract
Globally, water quality indices (WQIs) are beneficial for evaluating groundwater and surface water quality. The Canadian Council of Ministers of Environment Water Quality Index (CCME WQI) was combined with the parametric values given by Directive 98/83/EC to investigate the possible suitability of groundwater resources for human consumption on Rhodes Island. Chloride (Cl−), pH, calcium (Ca2+), electrical conductivity (CND), carbonate (CO32−), bicarbonate (HCO3−), potassium (K+), magnesium (Mg2+), sulfate (SO42−), sodium (Na+),nitrate (NO3−), nitrite (NO2−), ammonium (NH4+), and phosphate (PO43−) were included in the dataset applied in this study. Statistical analysis, GIS database, and WQI estimation were successfully used to evaluate the groundwater resources of the study area. All studied groundwater parameters have mean and median values lower than the corresponding parametric values established by Directive 98/83/EC. The high CND values (up to 2730 μs cm−1) in groundwater collected from Rhodes’ coastal aquifers indicate a direct relationship with seawater intrusion. CCME-WQI classifies the groundwater samples for most monitoring stations on Rhodes aquifers as “excellent”, Class 5, for 2019 and 2020. The findings of this study may be helpful for scientists and stakeholders monitoring the study area and applying measures to protect the groundwater resources.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".