Evaluation of microbial biofilm communities from an Alberta oil sands tailings pond
Bibliographic record
Abstract
Bitumen extraction from the oil sands of Alberta has resulted in millions of cubic meters of waste stored on-site in tailings ponds. Unique microbial ecology is expected in these ponds, which may be key to their bioremediation potential. We considered that direct culturing of microbes from a tailings sample as biofilms could lead to the recovery of microbial communities that provide good representation of the ecology of the tailings. Culturing of mixed species biofilms in vitro using the Calgary Biofilm Device (CBD) under aerobic, microaerobic, and anaerobic growth conditions was successful both with and without the addition of various growth nutrients. Denaturant gradient gel electrophoresis and 16S rRNA gene pyrotag sequencing revealed that unique mixed biofilm communities were recovered under each incubation condition, with the dominant species belonging to Pseudomonas, Thauera, Hydrogenophaga, Rhodoferax, and Acidovorax. This work used an approach that allowed organisms to grow as a biofilm directly from a sample collected of their environment, and the biofilms cultivated in vitro were representative of the endogenous environmental community. For the first time, representative environmental mixed species biofilms have been isolated and grown under laboratory conditions from an oil sands tailings pond environment and a description of their composition is provided.
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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) | 0.000 | 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".