Assessment of drinking water suitability of Vellayani freshwater lake South India
Bibliographic record
Abstract
Water is a vital component in the origin of life. The present study was undertaken to evaluate the physico-chemical and microbiological characteristics of Vellayani fresh water Lake located at south west coast of Kerala in South India. An assessment of heavy metal contamination in the lake is carried out. For the study, five representative samples were collected from Vellayani fresh water lake during the month of August 2024 (Post Monsoon season). The physico-chemical characteristics of water were analysed following the standard procedures in APHA (2024). The bacteriological quality was analyzed using the standard procedures (MPN method). The concentration of heavy metals was detected in AAS. The results show that the recorded values for turbidity, total coliforms, heavy metals like Fe and Mn were above the drinking water quality standard (BIS, 2012)4. Based on the Canadian drinking water quality standard (1992)7, concentration of hydrogen sulphide in the water samples also shows above the permissible limit. The study reveals that anthropogenic activities like sewage discharge and agriculture cause a high degree of pollution in the Vellayani freshwater lake. The high level of microbial contamination may be due to the leaching of waste water from nearest soak pit/septic tanks.
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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 | 0.001 |
| 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".