Spatial interpolation approach-based appraisal of groundwater quality of arid regions
Bibliographic record
Abstract
Abstract The primary objective was appraisal of groundwater quality during pre- and post-monsoon seasons for irrigation purposes. Good quality groundwater is required for high crop yields in arid regions. A total of 45 samples were collected from wells and analyzed in the laboratory for this research work. Different water quality parameters were determined from these samples, namely electrical conductivity (EC), sodium adsorption ratio (SAR), residual sodium carbonate (RSC), sodium percent (Na%), and permeability index (PI) during the pre- and post-monsoon season. The water types were identified through a Piper-trilinear diagram. Fifty per cent of the water samples of the total basin area fall under the saline category. The local farmers heavily rely on groundwater for the irrigation of crops. Excess use of groundwater for irrigation raises soil salinity. Some parts of the study area are facing serious problems such as loss of crop yields, and low availability of good quality groundwater even for drinking purposes. The results highlight that the study area has a high salinity content (C3) and low sodium (S1). The maps for different water quality parameters were generated using inverse distance weighted (IDW) interpolation method in ArcGIS 10.3 software.
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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 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| 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".