Impact of pharmaceutical industry treated effluents on the water quality of river Uppanar, South east coast of India: A case study
Bibliographic record
Abstract
The water quality of a river that received pharmaceutical industrial effluents is evaluated through the analysis of two indices to describe the level of pollution of the river, in this paper. The indices have been computed from December 2009 to June 2011 at four sampling stations—outlet, outfall, upstream, and downstream in the Uppanar River located at Cuddalore (South east coast of India). The results were compared with the guidelines of Bureau of Indian standards for drinking water specifications (BIS 10500).The study also identifies the pollutants of pharmaceutical industrial effluents before and after treatment that affects the river water quality. Data on spatial and temporal changes in dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, pH, temperature, color, electrical conductance, total dissolved solids, total suspended solids, calcium, magnesium, hardness, sodium, and chloride were collected. The water quality indices used, Bascarón ( 1979 ) adapted Water Quality Index (WQI BA ) and the Canadian Council of Ministers of the Environment-Water Quality Index 1.0 (CCME WQI), which is a well-accepted and universally applicable computer model for evaluating the water quality index. Both the indices presented similar trends, and were considered adequate for evaluating the impacts of industrial effluent on the river water bodies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".