Bibliographic record
Abstract
Water quality monitoring is an important aspect of water management concerning to the pollution control. Present communication deals with study of physico-chemical parameters such as pH, temperature , total dissolved solids (TDS),total suspended solids (TSS), total alkalinity (TA), , total hardness (TH), chloride (Cl), fluoride (F), sodium (Na), calcium (Ca), magnesium (Mg), sulphate (SO4), nitrate(NO3), nitrite (NO2), dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD) and heavy metals like chromium (Cr), arsenic (As), lead (Pb) and cadmium (Cd) of water samples from five sampling sites of River Tapti, Surat, Gujarat state, India. The water samples were collected at regular interval of 30 days for two consecutive years 2008-2009. The experimental values of water samples were compared with standard values given by World Health Organization (WHO). Water Quality Index (WQI) was also calculated to know the overall quality of water samples by using Water Rating System adapted by Canadian Council for Minister of the Environment (CCME-WQI). The data were analyzed statistically to identify those parameters which are responsible for variation in Water Quality Index (WQI). To identify those parameters we have done correlation analysis, testing of hypothesis, factor analysis and regression Analysis by using SPSS (Statistical Package for Social Science). The results show that the quality of water is poor and not good for drinking or irrigation purpose. The variance was found significant at 1% level of significance in case of and turbidity, BOD, Cr and Pb. It was found significant at 5% level in case of COD.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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) | 0.002 | 0.001 |
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".