Hydrocarbonoclastic biofilms based on sewage microorganisms and their application in hydrocarbon removal in liquid wastes
Bibliographic record
Abstract
Attempts to establish hydrocarbonoclastic biofilms that could be applied in waste-hydrocarbon removal are still very rare. In this work, biofilms containing hydrocarbonoclastic bacteria were successfully established on glass slides by submerging them in oil-free and oil-containing sewage effluent for 1 month. Culture-dependent analysis of hydrocarbonoclastic bacterial communities in the biofilms revealed the occurrence of the genera Pseudomonas, Microvirga, Stenotrophomonas, Mycobacterium, Bosea, and Ancylobacter. Biofilms established in oil-containing effluent contained more hydrocarbonoclastic bacteria than those established in oil-free effluent, and both biofilms had dramatically different bacterial composition. Culture-independent analysis of the bacterial flora revealed a bacterial community structure totally different from that determined by the culture-dependent method. In microcosm experiments, these biofilms, when used as inocula, removed between 20% and 65% crude oil, n-hexadecane, and phenanthrene from the surrounding effluent in 2 weeks, depending on the biofilm type, the hydrocarbon identity, and the culture conditions. More of the hydrocarbons were removed by biofilms established in oil-containing effluent than by those established in oil-free effluent, and by cultures incubated in the light than by those incubated in the dark. Meanwhile, the bacterial numbers and diversities were enhanced in the biofilms that had been previously used in hydrocarbon bioremediation. These novel findings pave a new way for biofilm-based hydrocarbon bioremediation, both in sewage effluent and in other liquid wastes.
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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) | 0.000 | 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".