Growth responses of 20 boreal forest species to oil sands non‐segregating tailings: significance for reclamation
Bibliographic record
Abstract
Oil sands mining in northeastern Alberta, Canada, generates tailings containing sand, silt, clay, water, and residual bitumen with hydrocarbons. The impact of tailings on revegetation is a major environmental concern and poses a significant land reclamation challenge. Oil sands companies have recently developed technologies that use thickeners in combination with carbon dioxide to produce non‐segregating tailings (NST), to accelerate the consolidation of tailings while sequestering greenhouse gases. Effects of these tailings on plant re‐establishment have yet to be determined. We investigated the impact of NST on biomass and physiology of 20 boreal woody plant species grown in environmentally controlled growth rooms. The seedlings were grown for 8 weeks in NST capped with boreal forest topsoil or peat‐mineral soil mix or petroleum coke in 50‐cm‐long polyvinyl chloride pipes. We found that the biomass of balsam fir, beaked hazelnut, blueberry, green alder, Labrador tea, lowbush cranberry, paper birch, and raspberry was severely reduced by NST, while the effects on balsam poplar, Bebb's willow, chokecherry, dogwood, saskatoon, and white spruce were relatively weak. This was also the case for jack pine, which showed poor survival in NST. The negative impact of NST on plants can be largely explained by elevated sodium and decreased foliar nutrient concentrations. The impact of NST capping with petroleum coke on plant growth was non‐significant. Differences among species in their survival and biomass responses to NST appeared to be partly a reflection of their natural habitats. Findings from this study can help guide revegetation oil sands reclamation strategies.
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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 | 0.001 | 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 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".