Dual approach decontamination of lake quarry water
Bibliographic record
Abstract
This study presents an innovative wastewater treatment approach that combines hydrodynamic cavitation with ozonation, achieving remarkable efficiency and environmental sustainability. Addressing critical issues such as water scarcity and persistent pollutants, the research emphasises the need for advanced, eco-friendly treatment technologies. The effectiveness of this novel method was tested on greywater sourced from a lake quarry in India. Over a 3-week period, the wastewater was treated daily for approximately 7 h, leading to substantial improvements in water quality. Notably, within the first week, the initial green discoloration and unpleasant odour were completely eliminated without the use of chemical compounds. The bacterial load was notably reduced from 105 colony-forming units (CFUs)/mL to 102 CFUs/mL, showcasing the treatment’s strong disinfection capabilities. In addition, the chemical oxygen demand dropped from 110 ppm to below 10 ppm, whereas the biological oxygen demand was reduced from 55 ppm to undetectable levels. Minor deviations were detected in chloride concentration, total dissolved solids, clarity, and water hardness. These results demonstrate the considerable potential of the combined cavitation–ozonation method for transforming contaminated, tarnished, and foul-smelling water into clean water, suitable for various practices.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".