Correlation, Regression Analysis, and Spatial Distribution Mapping of WQI for an Urban Lake in Noyyal River Basin in the Textile Capital of India
Bibliographic record
Abstract
Nowadays, the major threat to humans occurs due to water quality deterioration. The quality of water creates a new sign for the public to prevent them from waterborne diseases. This study uses sensitive water quality parameters obtained from the northeast monsoon season, October 2021, at different locations in Mooli Kulam lake (11°07′17.6″ N, 77°22′59.9″ E) of Tiruppur District, Tamil Nadu, India. The parameters considered for the analysis of lake water quality are closely included with drinking and irrigation parameters. The northeast monsoon samples collected from the lake were analysed and the Water Quality Indexing was applied to the dataset using three methods, namely, the Weight Arithmetic method, the Canadian Council of Ministers of the Environment, and Horton’s method. The parameters are divided into drinking water variables and irrigation water variables. This study includes water quality index mapping using Inverse Distance Weighting interpolation of the spatial distribution method using ArcMap 10.8. The dataset was subjected to correlation and regression analysis in order to determine the most significant pollutant. A total of 10 sampling stations and 23 water quality parameters have been analysed. The results obtained show that the lake has high eutrophication with compounds of potassium, iron, and nitrates.
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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 | 0.002 |
| 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) | 0.001 | 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".