Estimation of Water Quality and Physico-chemical Parameters of a Freshwater Lake
Bibliographic record
Abstract
The present study was undertaken to know the variation in different seasons in response to physico chemical properties of Laknavaram fresh water lake reservoir situated at Laknavaram village in Mulugu district Warangal. The study was carried out over a period of one year. In India there are enormous number of natural and manmade water bodies used for various purposes, mainly for drinking and agriculture. One of the most severe problems in arid and semi-arid regions is high concentration of salts in soils and water resources. Thus, water quality and its management have received much attention in developing countries. The present study is aimed at assessing the Water Quality Index of reservoir water and assesses the impact of industries and human activities. Physicochemical parameters were monitored for the calculation of Water Quality Index for the monsoon, winter and summer seasons. Seasonal changes in physico- chemical parameters such as water temperature, pH, turbidity, transparency, total dissolved solids, total hardness, chlorides, phosphate, nitrates, dissolved oxygen and biological oxygen demand were analyzed for a period of one year from September 2021 to August 2022. The results indicated that physico-chemical parameters of the water were within the permissible limits.The results indicate that the lake water is Non-polluted and can be used for Domestic, Irrigation and Pisciculture.
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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 | 0.000 | 0.001 |
| 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 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".