Benzene and Naphthalene Degrading Bacterial Communities in an Oil Sands Tailings Pond
Bibliographic record
Abstract
Oil sands process-affected water (OSPW), produced by surface-mining of oil sands in Canada, is alkaline and contains high concentrations of salts, metals, naphthenic acids, and polycyclic aromatic compounds. Residual hydrocarbon biodegradation occurs naturally, but little is known about the hydrocarbon-degrading microbial communities present in OSPW. In this study, aerobic oxidation of benzene and naphthalene in the surface layer of an oil sands tailings pond were measured. The potential oxidation rates were 4.3 μmol L-1 OSPW d-1 for benzene and 21.4 μmol L-1 OSPW d-1 for naphthalene. To identify benzene and naphthalene-degrading microbial communities, metagenomics was combined with stable isotope probing (SIP), high-throughput sequencing of 16S rRNA gene amplicons, and isolation of microbial strains. SIP using 13C-benzene and 13C-naphthalene detected strains of the genera Methyloversatilis and Zavarzinia as the main benzene degraders, while strains belonging to the family Chromatiaceae and the genus Thauera were the main naphthalene degraders. Metagenomic analysis revealed a diversity of genes encoding oxygenases active against aromatic compounds. Although these genes apparently belonged to many phylogenetically diverse taxa, only a few of these taxa were predominant in the SIP experiments. This suggested that many members of the community are adapted to consuming other aromatic compounds, or are active only under specific conditions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".