Degradation of a Mixture of Hydrocarbons, Gasoline, and Diesel Oil Additives by <i>Rhodococcus aetherivorans</i> and <i>Rhodococcus wratislaviensis</i>
Bibliographic record
Abstract
Two strains, identified as Rhodococcus wratislaviensis IFP 2016 and Rhodococcus aetherivorans IFP 2017, were isolated from a microbial consortium that degraded 15 petroleum compounds or additives when provided in a mixture containing 16 compounds (benzene, toluene, ethylbenzene, m-xylene, p-xylene, o-xylene, octane, hexadecane, 2,2,4-trimethylpentane [isooctane], cyclohexane, cyclohexanol, naphthalene, methyl tert-butyl ether [MTBE], ethyl tert-butyl ether [ETBE], tert-butyl alcohol [TBA], and 2-ethylhexyl nitrate [2-EHN]). The strains had broad degradation capacities toward the compounds, including the more recalcitrant ones, MTBE, ETBE, isooctane, cyclohexane, and 2-EHN. R. wratislaviensis IFP 2016 degraded and mineralized to different extents 11 of the compounds when provided individually, sometimes requiring 2,2,4,4,6,8,8-heptamethylnonane (HMN) as a cosolvent. R. aetherivorans IFP 2017 degraded a reduced spectrum of substrates. The coculture of the two strains degraded completely 13 compounds, isooctane and 2-EHN were partially degraded (30% and 73%, respectively), and only TBA was not degraded. Significant MTBE and ETBE degradation rates, 14.3 and 116.1 mumol of ether degraded h(-1) g(-1) (dry weight), respectively, were measured for R. aetherivorans IFP 2017. The presence of benzene, toluene, ethylbenzene, and xylenes (BTEXs) had a detrimental effect on ETBE and MTBE biodegradation, whereas octane had a positive effect on the MTBE biodegradation by R. wratislaviensis IFP 2016. BTEXs had either beneficial or detrimental effects on their own degradation by R. wratislaviensis IFP 2016. Potential genes involved in hydrocarbon degradation in the two strains were identified and partially sequenced.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".