Capping dewatered oil sands fluid fine tailings with salvaged reclamation soils at varying depths to grow woody plants
Bibliographic record
Abstract
Managing fluid fine tailings (FFT) present a major cause of industrial and environmental concerns in oil sands surface mining production. A potential management solution is to dewater and cap the FFT solids for use in land reclamation. A 16 wk greenhouse study was conducted to assess whether FFT centrifuge cake with caps of various reclamation soil mixes (forest floor mineral mix, peat mineral mix, and a mixture of both) and depths (0, 5, 10, and 20 cm) would support growth of trembling aspen (Populus tremuloides — native broadleaf tree) and beaked willow (Salix bebbiana — native broadleaf shrub). Beaked willow had a much greater survival rate (100%) when grown directly in FFT cake compared with trembling aspen (16.7%). Plants grown directly in FFT cake were negatively impacted by high water content, low nitrate supply rates, and high metal concentrations with beaked willow seedlings having 10 times higher foliar concentrations of Al, Cr, and Ti compared with any other treatments. Adding soil caps substantially increased aboveground biomass for both species, but differences among soil cap types and depths did not have as significant of an effect on plant growth. Results from this study show that capping FFT substantially improves woody plant growth, and S. bebbiana and P. tremuloides are potentially suitable species for tailings reclamation.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".