Assessment of groundwater intrinsic vulnerability using GIS-based DRASTIC method in district Karak, Khyber Pakhtunkhwa, Pakistan
Bibliographic record
Abstract
The Study area lies in the southern Kohat deformed fold and thrust belt. This part of the Kohat plateau, borders the southern extension of the Himalayan deformation, with the Salt range to the south most. The research is based on DRASTIC model. Anthropogenic activities have the potential to pollute groundwater. An essential component of managing groundwater is vulnerability mapping. This study used the DRASTIC model to analyze aquifer vulnerability and identify the hydrogeological condition in the southern portion of the Karak, Khyber Pakhtunkhwa. For the models, the information layers were provided via geographic information systems (GIS). The DRASTIC model uses seven environmental parameters. Vulnerability index concentrations were found to be 0.78% for Very Low vulnerability, 9.57% for Low vulnerability, 24.96% for Moderate vulnerability, 54.01% for High vulnerability, and 10.68% for Very High vulnerability, according to the results. A total 164.446 km 2 of the total 1,540 km 2 area is covered by the Very High vulnerable zone. The highest Nitrate concentration recorded in the area is 11 ppm and lowest is 4.4 ppm. Around 45% of the samples surpassed the approved limit of PSQWA (Pakistan Standards and Quality Control Authority) and NSQWQ (National Standards for Quality of Water) standard. The concentration of Nitrate >10 ppm represent that some human action has contributed toward the highest concentration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".