Indigenous microbial communities in Albertan sediments are capable of anaerobic benzene biodegradation under methanogenic, sulfate‐reducing, nitrate‐reducing, and iron‐reducing redox conditions
Bibliographic record
Abstract
Abstract Alberta is a major center for oil and gas production, and correspondingly harbors hundreds of unresolved contamination sites by environmental hazards such as benzene (C6H6). Due to its cost‐effectiveness, bioremediation has become a promising strategy for C6H6 removal. Contamination sites typically take on an anaerobic context, which complicates the energetics of contamination sites and is a subject that is scarcely broached in studies of Albertan sediments. This study examines the innate potential for indigenous microbial communities in Albertan sediments to remove C6H6 in a multitude of reduced conditions. Community profiles of these sediments were analyzed by 16S rRNA gene amplicon sequencing, and removal rates and reaction stoichiometries were observed by gas chromatography and ion chromatography. Organisms belonging to known primary degrader taxa were identified, including Geobacter (iron‐reducing), and Peptococcaceae (nitrate‐reducing). Furthermore, benzene removal patterns of the cultures were similar to those observed in previously reported microcosms, with lag times between 70 and 168 days and removal rates between 3.27 and 12.70 µM/day. Such information could support a more comprehensive survey of Albertan sediment consortia, which may eventually be utilized in informing future remediation efforts in the province. Practitioner points Clay and sand sediments originating from Northern Alberta could remove benzene under methanogenic, sulfate‐reducing, iron‐reducing, and nitrate‐reducing conditions. Degradation profiles were broadly comparable to those of reported cultures from other geographical locales. Key degrader taxa observed included Geobacter (Fe3+‐reducing) and Peptococcaceae ( ‐reducing). Knowledge gained can be the start of a more extensive survey of Albertan sediments. Eventually, this collection of information can be used to generate robust C6H6‐degrading cultures that can be implemented for bioaugmentation and be implemented in informing remediation strategies in soil and water matrices for priority contamination cases such as leaking underground storage tanks and orphan wells.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".