GROUND WATER QUALITY ASSESSMENT USING WATER QUALITY INDEX: CASE STUDY OF AYUB AGRICULTURE RESEARCH INSTITUTE, FAISALABAD, PAKISTAN
Bibliographic record
Abstract
Poor water quality index for groundwater in Pakistan is a pressing issue due to the increasing population, rapid urbanization, and industrialization. This has resulted in fertile and productive soil being converted to barren land, leading to significant reductions in infiltration rates and agricultural productivity. The aim of the study was to assess water quality monitoring in research areas of AARI using Water Quality Index (WQI) during the year 2023-24. Collected water samples (12 different sites) were analyzed at Soil and Water Testing Laboratory for Research, Faisalabad for WQI by using various physicochemical characteristics like EC, TDS, Cl, SAR and RSC. Electrical Conductivity (EC) for all 12 samples varied between 787 to 3529 µS/cm. On the basis of Fitness criteria water samples from only 2 sites S1 and S8 were under “fit” category, 3 sites (S4, S5, S12) were under “marginally fit” and remaining all samples were “unfit” for Irrigation because of high salt concentration (EC > 1250 µS cm -1 ). The WQI values obtained from the research areas ranged from 68.05 to 354.4. Samples were classified using the WQI classification criteria and it revealed that only 25% of the samples of the area were categorized as “good (WQI range 50-100),” 50% were rated as “poor” (WQI range 101-200), and the remaining 25% were classified as “very poor” (WQI range 201-300) quality water.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".