Investigation of Groundwater Resources Quality for Drinking Purposes Using GWQI and GIS: A Case Study of Ottawa City, Ontario, Canada
Bibliographic record
Abstract
Evaluating groundwater quality for certain purposes requires accurate quantitative and qualitative management, accessibility to the study area, and knowledge of the governing environmental processes. Groundwater resources are used to supply drinking water consumption alongside surface water in most countries. This study aims to investigate the quality of groundwater resources in the city of Ottawa, located in Ontario, Canada, using the Schoeller diagram and the Canadian Groundwater Quality Index (GWQI) in a fuzzy environment. To determine the water quality, the qualitative groundwater parameters including Ca, Mg, Na, Cl, SO4, HCO3, NO3, F, pH, TDS, TH, K, EC, and Alkalinity were considered in the Schoeller diagram and GWQI. Each parameter’s interpolated water quality map layer was prepared using the Kriging method in a GIS environment. The results of Schoeller’s diagram indicated that the range of drinking water quality was non-potable to inappropriate in more than 22% of the investigated groundwater resources. Moreover, the obtained results of the groundwater quality interpolation map layer based on the GWQI revealed that more than 70% of the groundwater resources were examined in the good and excellent range for drinking purposes. Finally, the obtained interpolated map layers of the Schoeller diagram and GWQI were integrated using GIS. Accordingly, the results indicate that the interpolation values of an integrated layer in the study area are well within the permissible limits, and the quality of the groundwater is suitable for drinking and other consumption purposes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".