The microbiology of oil sands tailings: past, present, future
Bibliographic record
Abstract
Surface mining of enormous oil sands deposits in northeastern Alberta, Canada since 1967 has contributed greatly to Canada's economy but has also received negative international attention due largely to environmental concerns and challenges. Not only have microbes profoundly affected the composition and behavior of this petroleum resource over geological time, they currently influence the management of semi-solid tailings in oil sands tailings ponds (OSTPs) and tailings reclamation. Historically, microbial impacts on OSTPs were generally discounted, but next-generation sequencing and biogeochemical studies have revealed unexpectedly diverse indigenous communities and expanded our fundamental understanding of anaerobic microbial functions. OSTPs that experienced different processing and management histories have developed distinct microbial communities that influence the behavior and reclamation of the tailings stored therein. In particular, the interactions of Deltaproteobacteria and Firmicutes with methanogenic archaea impact greenhouse gas emissions, sulfur cycling, pore water toxicity, sediment biogeochemistry and densification, water usage and the trajectory of long-term mine waste reclamation. This review summarizes historical data; synthesizes current understanding of microbial diversity and activities in situ and in vitro; predicts microbial effects on tailings remediation and reclamation; and highlights knowledge gaps for future research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".