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Record W4415478788 · doi:10.1680/jenes.24.00039

Dual approach decontamination of lake quarry water

2025· article· en· W4415478788 on OpenAlexvenueno aff
Amarsinh L. Jadhav, Parvez A. Gardi

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2025
Typearticle
Languageen
FieldMaterials Science
TopicUltrasound and Cavitation Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsChemical oxygen demandWastewaterHuman decontaminationBiochemical oxygen demandGreywaterSewage treatmentWater treatmentWater scarcity

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.138

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.003
GPT teacher head0.180
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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