Bioremedial Approch to Degrade Physico-Chemical Characteristics by Indigenous Microbes in Paper and Pulp Industry
Bibliographic record
Abstract
Recent studies have found paper and pulp industries responsible for polluting the environment in India by releasing hazardous liquids which contain heavy metals and other toxicants.The effluents released from these industries, pollute the water bodies.The polluted water bodies contain compounds which are toxic to aquatic flora and fauna as well as have a strong mutagenic effect.Bioremediation may serve as an appropriate method to reduce the physico-chemical parameters to a prescribed Limit by CPCB.Biological treatment has been reported efficacious in reducing the organic load and toxic effects of kraft mill effluents.The present investigation was aimed to degrade physicchemical characteristics from effluent generated by Pulp and Paper industry.The physico-chemical analysis of effluents showed that these characteristics were notably high which was not permissible by CPCB and ISI.Based on the isolation, identification and biochemical characterization studies the isolated bacterial strain was identified as Bacillus sp.Simulated approach was utilized to monitor Physicochemical properties (DO,COD, BOD, Alkalinity, Acidity, Chloride, Hardness, Nitrate, Phosphate) after bacterial treatment.A reduction in all the physico-chemical properties was observed with post bacterial treatment which was in accordance with the standards prescribed.
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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 | 0.001 | 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) | 0.002 | 0.001 |
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".