Evaluating semi-arid lake water quality. A synergy of water quality indices, multivariate statistics and geospatial technology
Bibliographic record
Abstract
Water pollution Principal component analysis The Canadian Council of Ministers of Environment water quality index Industrial water quality index Gubbi lake TumakuruA study was conducted on Gubbi Lake to investigate the water quality using water quality indices (WQI), multivariate statistical technique and geospatial technology.20 lake water samples were collected during premonsoon and post-monsoon seasons for examining physicochemical parameters.The results revealed that Biochemical Oxygen Demand measured in milligram per litre (8.5 mg/l and 5.3 mg/l in pre-monsoon and post-monsoon season respectively) exceeded the normal range of 5 mg/l and ammonia (1.24 mg/l and 0.6 mg/l during pre-monsoon and postmonsoon season respectively) exceeded acceptable limits recommended by the Bureau of Indian Standards in both seasons.The Canadian Council of Ministers of Environment WQI ranged from 66.7 to 81.13 with a recorded mean of 74.22 imparting 'fair' conditions.Apart from Kelly's index, all the irrigation WQIs designated majority of water samples as suitable for irrigation.All the industrial WQIs conveyed the tendency to corrode except Larson and Skold index that indicated corrosion potential.The principal component analysis effectively diminished the complex water analysis dataset into 6 principal components each for pre-monsoon and postmonsoon seasons which explained 87.85 % and 89.80 % of total variance respectively.These components identified the pollution sources as primarily originating from anthropological activities like agricultural runoff, domestic sewage waters and natural weathering of rocks.Hence, the combined approach using above-mentioned methodologies proves to be indispensable in evaluating surface water quality.The findings of this study further underscore the necessity for prompt action by decision makers for well-being of both environment and public health.
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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 | 0.002 | 0.002 |
| 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) | 0.000 | 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".