Multivariate analysis of inland water quality index in parts of Vapi district, Gujarat, India
Bibliographic record
Abstract
ABSTRACT Water resource management substantially depends on water quality (WQ). Anthropogenic and geogenic pollutants in water system are challenging to identify, transport, and properly dispose of, thus demanding frequent monitoring. Study focuses on application of statistical approach to analyse pattern and to monitor WQ parameters of region. Paper presents computation of water quality index (WQI) based on various WQ parameters of the Daman Ganga River situated in Vapi, Gujarat, India. 17 WQ parameters considered were pH, electrical conductivity, temperature (Temp), total dissolved solids, NO2 + NO3, (P-Tot), Ca, Mg, Na, K, Cl, SO4, CO3, HCO3, total hardness, sodium absorption ratio (SAR), and calcium hardness (HAR_Ca). Quartile deviation was carried out as preprocessing technique to identify fair analysis of trend followed by other parameters. Application of PCA followed by varimax rotation factor analysis was attempted to identify contribution of significant parameters. Methods developed by Council of Canadian Ministry of Environment (CCME) and British Columbia (BC) were applied to compute WQI. WQI evaluated were 42.35 and 63.29 for CCME and BC, respectively, based on five significantly influencing parameters, namely, HAR_Ca, SAR, CO3, Temp, and P-Tot. Study signifies the hardness and salinity factors impacting WQ and efficiently reduces subjectivity and bias to determine the WQI model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".