Microbially-mediated fugitive gas production from oil sands tailings and increased tailings densification rates
Bibliographic record
Abstract
Oil sands in northeastern Alberta, Canada, are strip mined and bitumen is separated from sand and clay by an alkaline hot water extraction process leaving millions of cubic metres of tailings for disposal. These include mature fine tailings (MFT) collected in settling ponds and consolidated or composite tailings (CT) that are produced by the addition of sand and gypsum to hasten dewatering and densification. This laboratory investigation monitored the potential for fugitive gas production in 0.9-m high columns that contained MFT or CT samples from three oil sands companies. Methane was found in columns that contained aged MFT, but not in those that contained fresh MFT. Dissolved or entrapped methane was found in columns containing each of the MFT samples and two of the CT samples. Ethylene was detected in columns containing MFT or CT. This may affect future plans to re-vegetate disturbed areas of the oil sands operations because ethylene strongly influences many phases of plant development. The densification rate of a methanogenic MFT was faster than that of a nonmethanogenic MFT that was placed in a similar column 6 years prior to the start of this investigation, suggesting that methane formation may increase the rate of densification. Key words: consolidated tailings, composite tailings, densification, ethylene, methane, methanogens, oil sands, sulfate-reducing bacteria.
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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 | 0.000 |
| 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".