Water quality assessment of Kavvayi Lake of northern Kerala, India using CCME water quality index and biological water quality criteria.
Bibliographic record
Abstract
Assessment of water quality status of 7 sites of Kavvayi Wetland in northern Kerala (India) was carried out. The physico-chemical, bacteriological and biological parameters were monitored during pre-monsoon, monsoon and post-monsoon seasons. Canadian Council of Ministers of the Environment (CCME) water quality index of the Kavvayi Lake samples ranged from 43.99-44.77; indicating that water quality was threatened or impaired. The poor water quality status might be due to dumping of wastes from municipal and domestic sources and agricultural runoff. Biological water quality criteria (BWQC) determined for wetland revealed that stations such as mixing point of Kariangode River into Kavvayi Lake and Kottikkadavu was moderately polluted in pre-monsoon and post- monsoon seasons. Mixing point of Nileswar River into Kavvayi Lake was moderately polluted in pre-monsoon season. Both calculated indices suggest that quality of lake was found to be influenced by anthropogenic activities such as unscientific tourism and infrastructure development, land encroachment, sand mining, pollution etc. The study was carried out as part of a programme, which aimed to conserve Kavvayi wetland because of its unique ecological and environmental characteristics.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".