Biostimulation of Indigenous Ureolytic Microbes in Mature Fine Tailings for Reclamation: A Pilot Laboratory Study with Environmental Variables Considered
Bibliographic record
Abstract
More than one-billion-m3 of mature fine tailings (MFTs) in Canada need decades to be reclaimed because of their stable “card-house” structure. Previous studies demonstrated that augmented microbially induced carbonated precipitation (MICP) accelerated the settlement process and flocculated the fabric of MFTs. However, cultivating enormous amounts of bacteria to treat considerable volumes of MFTs using augmented-MICP is an impractical approach. Although MFTs harbor a diversity of microorganisms, the implementation of indigenous ureolytic microbes in MFTs has never been elucidated. Thus, we explored if such microbes in MFT could be stimulated to densify MFT via investigating the effects of urea and oxygen availability, pH, and inhibition of methanogens on the MICP treatments. The results demonstrated that only under the initial conditions with oxygen, urea, and neutral pH, the MICP-treated MFT specimens developed a thick heavy precipitate layer, enhancing bearing capacity through the stimulated ureolytic microbes. Precipitation and ammonium accumulated since day 4 indicating the activities of the ureolytic microbes. The final average precipitation content reached to 8.1% on day 30 of the stimulated-test group. Owing to the ureolytic microbes facilitating urea degradation and calcite bonding on soil particles, MFT’s “card-house” structure was transformed into a compact precipitated fabric. We speculated that Bacillus played a primary role for developing the ureolytic MICP treatment in MFT due to the microbial community analysis. The easily accessible MICP method for densifying MFT by stimulating indigenous ureolytic microorganisms enable an alternative to augmented-MICP and other treatments, which will reduce the time and difficulty of the reclamation process of MFT. Thus, the results of this pilot laboratory study can lead to an innovative and sustainable approach for MFT reclamation.
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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 | 0.001 | 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) | 0.001 | 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".