“Spatial Data Analysis for Ground Water Quality Assessment With Special Reference to Fluoride” - A Case Study of Dhar District, Madhya Pradesh, India
Bibliographic record
Abstract
Water is a prime natural resource and physiological necessity to mankind. Therefore, drinking water must not carry harmful chemicals as well as biological contaminants for the well-being and human health. Some of the chemicals like Fluoride, Iron, Arsenic, Cadmium, Chromium, Lead, selenium, and Nitrate in water may produce serious physiological changes when exist beyond permissible concentration. The Aim of the study was to create spatial map for drinking water purpose for Dhar district, MP. The ground water quality data were collected from PHED, M.P. and IMIS (Ministry of Drinking water & Sanitation) Website,Government of India. The GWQ layers were created separately for each element for Pre-Monsoon and Post-Monsoon period from the well point layers with interpolation technique. Each element wise layer has been categorised into `three catagories (1) potable water in Desirable limits (2) Potable water in permissible limits (3) non-potable ground water, as per BIS standard, 2015. The Union of eight element layers of each pre-monsoon and post-monsoon has been done and integrated pre-monsoon and Integrated post-monsoon Ground Water Quality (GWQ) map have been prepared and after the Union of these two maps, the Final ground water quality map has been prepared. It was concluded from the study, that multiple parameters are affecting the quality of ground water in Dhar district and particularly excess Fluoride, Nitrate, Total Hardness (as Caco3), Iron, pH, and Total Dissolve solids are prevalent in the area. About 69.66% Habitation of Dhar district is severely affected mainly by excess of Fluoride, Nitrate & Total Hardness (as Caco3), pH & Iron(Fe) & It is observed that about 70.51% area of Dhar district has been affected in terms of Ground Water Quality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".