An Assessment of Water Quality in River Periyar, Kerala, South India Using Water Quality Index
Bibliographic record
Abstract
River periyar of Eloor-Edayar industrial stretch has been a subject of pollution study for many years but so far, indexing of water quality has never been attempted.Indexing of water quality variables was carried out using water quality index method developed by Canadian Council of Ministry of Environment (CCME).Statistical techniques like correlation and regression using SPSS software was used to understand the relation between parameters and water quality index.Overall water quality index showed "Poor" quality index in the river, with each sampling site coming under the "poor" quality index range.Correlation analysis showed that water quality index decreases with increase in parameter concentration and vice versa for parameters like calcium, sulphate, chloride, nitrate-nitrogen, total hardness, fluoride, and conductivity.From the analysis, it was found that the water quality index range increases with increase in pH and dissolved oxygen.Regression analysis was used to identify the extend to which each factor; scope, frequency and amplitude, used in the calculation, influenced the water quality index.From the analysis, it was observed that the number of parameters that exceeds the guideline (Scope, F 1 ) and the number of times each parameter exceeding the guideline (Frequency, F 2 ) affects the water quality index of the river.While the extend to which each parameter exceeding the standard limit (amplitude, F3) does not affect the water quality index.
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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 | 0.000 | 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".