SURFACE WATER QUALITY ASSESSMENT AND MAPPING OF PERIYAR RIVER USING CANADIAN COUNCIL OF MINISTERS OF THE ENVIRONMENT WATER QUALITY INDEX METHOD
Bibliographic record
Abstract
In the view of higher demand of fresh water, water quality assessment has to be done periodically. In this study, the water quality of the Periyar River, which is located in Kerala, India is evaluated. River Periyar is considered as the lifeline of Kerala. Twentyfive percentage of Kerala's industries are located at the banks of Periyar River. Due to the disposal of untreated or partially treated waste in to the river, the water quality of the river changes. In order to make the water fit for drinking purpose, the water quality parameters should lie in permissible limit. The water quality data measurement results obtained from CPCB during the year 2019 was used as the input data. Water quality was evaluated by Canadian Council of Ministers of the Environmental Water Quality Index (CCME WQI) method. QGIS was adopted to map the WQI values. IDW interpolation technique was adopted for mapping. WQI of station numbers SS:2333 and SS:18 showed almost good WQ compared to other sampling stations as it is not an industrialized area. The flood happened in Kerala influenced a lot in the water quality. Station number SS:2333 showed a WQI of 83.8, which is the largest value obtained during the entire course of study. WQI of station number SS:17 is obtained as 51.36, which is the lowest among all results. From the study it is evident that CCME WQI is a good indicator for water quality assessment and can be used for similar type studies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".