Establishing and Optimizing a Bacterial Consortia for Effective Biodegradation of Petroleum Contaminants: Advancing Classical Microbiology via Experimental and Mathematical Approach
Bibliographic record
Abstract
In classical microbiology, developing a high-efficiency bacterial consortium is a great challenge for faster biodegradation of petroleum contaminants. In this study, a systematic experimental and mathematical procedure was adopted to establish a bacterial consortium for the effective biodegradation of heavy oil constituents. A total of 27 bacterial consortia were established as per orthogonal experiments, using 8 petroleum-degrading bacterial strains. These bacteria were closer phylogenetic relatives of Brevundimonas sp. Tibet-IX23 (Y1), Bacillus firmus YHSA15, B. cereus MTCC 9817, B. aquimaris AT8 (Y2, Y6 and Y7), Pseudomonas alcaligenes NBRC (Y3), Microbacterium oxydans CV8.4 (Y4), Rhodococcus erythropolis SBUG 2052 (Y5), and Planococcus sp. Tibet-IX21 (Y8), and were used in different combinations. Partial correlation analysis and a general linear model hereafter were applied to investigate interspecific relationships among different strains and consortia. The Y1 bacterial species showed a remarkable synergy, whereas Y3, Y4, and Y6 displayed a strong antagonism in all consortia. Inoculation ratios of different strains significantly influenced biodegradation. An optimal consortium was constructed with Y1, Y2, Y5, Y7, and Y8, which revealed maximum degradation of 11.238 mg/mL OD600 for oil contaminants. This study provides a line of evidence that a functional consortium can be established by mathematical models for improved bioremediation of petroleum-contaminated environment.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".