GIS-based assessment of groundwater quality and its suitability for drinking and irrigation purpose in a hard rock terrain: a case study in the upper Kodaganar basin, Dindigul district, Tamil Nadu, India
Bibliographic record
Abstract
abstract This study emphasizes hydrogeochemistry and quality degradation of groundwater in the upper Kodaganar basin, located in Dindigul district, South India. The Kodaganar basin has a particular significance and requires great attention because groundwater is the only major source for domestic and irrigation consumption. Twenty wells in the basin are randomly identified for sampling groundwater. The standard sampling procedures and laboratory experiments are followed for analyzing each sample. Index representing the suitability of drinking water is estimated based on the recommendations of Canadian Council of Ministry of Environment. The spatial distribution of the suitability was prepared by inverse distance weighted method. The traces of pollution and sources of pollution analyzed through Piper diagram suggested the role of natural and anthropogenic causes. The Gibbs boomerang diagram for both season illustrated around 85% of anions concentration dominated by rock type and also 80% of cation concentration influenced by surface interactions. This survey concludes that the overall drinking suitability of groundwater is fair in 7 wells and good in 13 wells. Only nine wells are found suitable for irrigation and rest of the wells can be used for irrigation only after minor treatment.
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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 | 0.002 |
| 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".