Drinking Water Quality and Techniques for Recharging an Urban Water System - for the industrial city of Baroda, India
Bibliographic record
Abstract
The major part of Gujarat in Western India is arid to semi arid with an average annual rainfall of about 500-700 mm.Baroda is an important industrial center of India situated in Gujarat.The city has experienced a heavy influx of population with a rise in industrialization.This has re.sulted in scarcity of water in the city area.Water pollution has become a major issue in the development of surface and groundwater resources for the protection of the fragile ecosystem.In the present chapter the quality of drinking water, and techniques for water harvesting, are discussed.The main sources of pollution are the industrial effiuents from industries producing, for example, fertilizers, petrochemicals, pesticides, pharmaceuticals, corrosive materials, organic wastes etc.The domestic sector also produces solid and liquid wastes.In all, 26 physico-chemical parameters and a few heavy metals were analyzed season-wise for a period of 2 y in the five ponds of Baroda city, to study the impact of urbanization and industrialization on the quality of the water.Parameters like pH, turbidity, total hardness, sulfates, chlorides, fluorides, TDS, BOD, COD, DO etc were estimated and are discussed in the chapter.All the studied parameters were in higher concentrations than the control.This is because of biotic activities in the pond, such as the decomposition of Pandit, B.R. and H. Solanki.2004."Drinking Water Quality and Techniques for Recharging an Urban Water System-for the industrial city of Baroda, India."
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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 | 0.001 | 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".