Assessment of Khibiny Alkaline Massif groundwater quality using statistical methods and water quality index
Bibliographic record
Abstract
Abstract Groundwater is a major source for the water supply of households in the mining‐intensive area of Khibiny Alkaline Massif, Kola Peninsula, in the Arctic. There are an increasing number of signs of groundwater quality deterioration in the area caused by the presence of elevated aluminum concentrations. Groundwater quality studies using univariate and multivariate statistical methods and the Water Quality Index were conducted to analyze a field dataset including 12 groundwater quality parameters monitored between 1999 and 2012. Descriptive statistics showed that the monitored water did not meet the established drinking water standards for aluminum concentration and pH level. The calculated Spearman correlation coefficient matrix revealed statistically significant associations (α‐level = .05) between Al concentrations and pH values, concentrations of SO42−, NO3−, Cl−, and TDS. Factor analysis using the principal component analysis extraction method (FA/PCA) identified four major influencing factors. Altogether the factors captured 67.53% of the dataset total variance. The outcomes of the hierarchical cluster analysis (HCA) revealed that the 12 monitored groundwater quality parameters can be grouped into three clusters where the concentration of Al and pH level formed a separate cluster. The calculated score values of the Canadian Council of Ministers of the Environment Water Quality Index indicated a deterioration of groundwater quality over the monitoring period.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".