Mature fine tailings from oil sands processing harbour diverse methanogenic communities
Bibliographic record
Abstract
Processing oil sands to extract bitumen produces large volumes of a tailings slurry comprising water, silt, clays, unrecovered bitumen, and residual solvent used in the extraction process. Tailings are deposited into large settling basins, where the solids settle by gravity to become denser mature fine tailings (MFT). A substantial flux of methane, currently estimated at ~40 million L/day, is being emitted from the Mildred Lake Settling Basin. To better understand the biogenesis of this greenhouse gas, the methanogenic consortia in MFT samples from depth profiles in 2 tailings deposits (Mildred Lake Settling Basin and West In-Pit) were analyzed by constructing clone libraries of amplified archaeal and bacterial 16S rRNA genes. The archaeal sequences, whose closest matches were almost exclusively cultivated methanogens, were comparable within and between basins and were predominantly (87% of clones) affiliated with acetoclastic Methanosaeta spp. In contrast, bacterial clone libraries were unexpectedly diverse, with the majority (~55%) of sequences related to Proteobacteria, including some presumptive nitrate-, iron-, or sulfate-reducing, hydrocarbon-degrading genera (e.g., Thauera, Rhodoferax, and Desulfatibacillum). Thus, MFT harbour a diverse community of prokaryotes presumptively responsible for producing methane from substrates indigenous to the MFT. These findings contribute to our understanding of biogenic methane production and densification of MFT in oil sands tailings deposits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".