Predictions of GIS-based intrinsic and specific groundwater vulnerability indicators: A comparative study
Bibliographic record
Abstract
<!--!introduction!--><b></b> Determining the aquifer vulnerability to pollution is of paramount importance for devising precautionary groundwater protection measures and for land use planning. In this study, we aim to draw a comparison between the intrinsic vulnerability reflected in the hydrogeological conditions and specific vulnerability addressing the vulnerability of an aquifer to a specific contaminant. In consequence, we would be able to quantify their prediction accuracy. To that end, we quantified the groundwater vulnerability for an overexploited aquifer in southern India by means of three indicators including DRASTIC (Depth to water, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone, and hydraulic Conductivity), modified DRASTIC, and Susceptibility Index (SI). The accuracy of the methods was evaluated using a GIS-based index-overlay approach. Temporal variations in climatic conditions influence the fluctuations in groundwater levels during various seasons which has been rarely considered in aquifer vulnerability studies. This study incorporated, therefore, the spatio-temporal variation in groundwater level in predicting intrinsic and specific vulnerability. Sensitivity and uncertainty analyses were conducted to assess the relative importance of input parameters required for the indicators. Results showed that the lithology of the aquifer was found to be the most sensitive input. A comparison of the three indicators demonstrated that the vulnerability maps from DRASTIC and SI showed the greatest difference. In contrast, modified DRASTIC and SI indicated the greatest similarity, which should be ascribed to the inclusion of the land use factor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".