Evaluation of boreal plant and microbe communities on tailings solvent recovery unit (TSRU) tailings: A meso-scale greenhouse study
Bibliographic record
Abstract
Surface mining and bitumen extraction in Alberta's oil sands generates various tailings waste streams as by-products. Among these tailings, froth treatment tailings (FTT), originating from the froth treatment process, are particularly complex due to high levels of iron sulfide minerals (i.e. pyrite) with acid mine drainage (AMD) potential. Paraffinic FTT that are passed through a Tailings Solvent Recovery Unit (TSRU) to recover residual solvent are referred to as TSRU tailings. This study evaluated the ability of TSRU tailings to support upland and wetland vegetation under various thin capping designs using peat mineral soil mix (PMM) and coarse sand tailings (CST). A 30 cm PMM cover cap successfully supported upland and wetland vegetation for three years, while a thicker, multi-layer cap of 30 cm PMM above 20 cm of CST further improved plant survival and growth. The cap was found to effectively act as a barrier, protecting the vegetation from the adverse effects of pyrite oxidation in the TSRU tailings. While the TSRU tailings acidified (pH ≤ 2) under all capping treatments, the thickest cap (30 cm PMM/20 cm CST) delayed acidification by one year. TSRU and CST had low microbial biomass and diversity, with bacterial communities mostly composed of sulfur oxidizers and acidophilic taxa. In contrast, the PMM layer maintained a higher microbial biomass, diversity, and stability across all treatments, highlighting its potential to enhance plant-microbe interactions and improve the reclamation success of TSRU tailings.
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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 | 0.001 |
| 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 | 0.001 |
| 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".