Evaluation of surface water quality using water quality indices (WQIs) in Lake Sukhna, Chandigarh, India
Bibliographic record
Abstract
Abstract To assess the surface water quality of Sukhna Lake, 13 physico-chemical parameters like temperature, pH, transparency, dissolved oxygen, electrical conductivity, total dissolved salts, chloride, total Aalkalinity, total hardness, calcium, magnesium, nitrate and phosphate were investigated on monthly basis for a period of two year (July 2016–June 2018) by using standard procedures. The results were compared with the values or ranges mentioned by standard organizations (WHO and BIS) for assessing the water quality and these revealed that the lake water was turbid and under DO distress. Various water quality indices like water quality index (WQI), Canadian Council Ministry of Environment (CCME)-WQI and comprehensive pollution index (CPI) were used to assess the water quality status in the Sukhna Lake. The range of WQI (59.74–83.49) indicated that the water quality status of the lake belonged to good category while those of CCME-WQI (52.4–81.61) revealed that water quality fallen from marginal to good category and those of CPI (0.4–0.7) indicated fair state of water in the lake. Overall the water quality in Sukhna Lake has been found deteriorated during second year in comparison the first year during the study time.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".