Bibliographic record
Abstract
Current bitumen extraction technologies used in surface mined oil sands in Alberta, Canada require large volumes of fresh water, which in turn generate large volumes of fluid fine tailings (FFT). Managing these tailings is a major challenge for oil sands operators. Non-aqueous extraction (NAE) is an alternative method which uses hydrocarbon solvents such as cyclohexane, producing smaller volumes of dry tailings with most of the solvent being recoverable post-extraction. However, residual cyclohexane remains in the non-aqueous extraction solids; therefore, developing technology for cycloalkane biodegradation is the aim of this study. Microcosm experiments involve setting up sealed bottles in which a microbial source such as soil or FFT is mixed with nutrient media and/or NAE dry tailings. These microcosms were amended with distinct electron acceptors and cycloalkane NAE solvents to create aerobic, nitrate-reducing, sulfate-reducing, iron-reducing, or methanogenic conditions for cycloalkane biodegradation. These conditions simulated dry tailings management under either upland and wetland reclamation scenarios. Gas chromatography was used to measure cycloalkane concentrations in the microcosm. Electron acceptor depletion and gas production resulting from biodegradation were also being monitored over the course of the experiments. Microcosms containing active microbial communities capable of cycloalkane degradation were found to have three elements in common: maintaining aerobic conditions via oxygen addition, sufficient concentrations of nitrogen and phosphorus, and FFT inoculum. In all other treatments, including anaerobic conditions or other inoculates such as soil or oil sands process affected waters, no significant degradation was observed over the allotted 2 year incubation despite other indications of microbial activity. Therefore, future cycloalkane biodegradation technologies will likely require oxygen and nutrients for adequate cycloalkane removal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".