Microbially-mediated de-watering and consolidation (“Biodensification”) of oil sands mature fine tailings, amended with agri-business by-products
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Oil sands surface mining operations in northeastern, Alberta, Canada produce enormous volumes of fluid fine tailings, an aqueous suspension of fine clays, sand, unrecovered bitumen, and diluent hydrocarbons. The tailings are deposited and retained on-site in large settling basins where the colloidal solids sediment and consolidate very slowly by gravity and pore water collects at the surface for re-use. Tailings ‘biodensification’, mediated by indigenous microbes that produce methane and/or carbon dioxide, is a phenomenon observed in situ and in vitro whereby tailings with active anaerobic microbial communities consolidate and de-water faster than predicted by gravitational (self-weighted) consolidation alone. To exploit this phenomenon, we used organic amendments to stimulate endogenous anaerobic tailings microorganisms. Tailings from three different operators were amended with agri-business by-products, placed in 100-mL microcosms and 1.5-L settling columns, and monitored for methanogenesis, pore water recovery, and solids densification. Several amendments increased methane production and accelerated biodensification compared to unamended and negative controls. Hydrolyzed canola, blood meal, bone meal and glycerol generally accelerated biodensification, stimulated methane production and supported growth of methanogens and fermentative microbes. Amendment altered the chemistry of the tailings, generally decreasing pH, increasing conductivity and magnesium, potassium, sodium, and bicarbonate concentrations. Biodensification is a potential engineered technology for accelerating water recovery and reducing the volume of stored oil sands tailings.
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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 | 0.001 |
| 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 it