Effects of Nitrogen and Phosphorus Supplementation on Responses of Trembling Aspen and White Spruce Seedlings in Reclamation Soils Amended by Non-Segregating Oil Sands Tailings
Bibliographic record
Abstract
Oil sands mining in northeastern Alberta, Canada, has disturbed large areas of the northern boreal forest which must be restored to pre-disturbance levels through reclamation. The oil sands tailings have high pH and elevated levels of Na+ which are harmful to plants. A novel non-segregating tailing (NST) was developed to accelerate consolidation of fine tailings, yet its effects on boreal plant species are not well characterized. In oil sands reclamation, a capping layer—either forest mineral soil mix (FMM), salvaged from upland boreal forest sites, or peat mineral mix (PMM), sourced from peatlands—is typically applied over overburden materials and coarse tailings sands prior to revegetation. Plants in oil sands revegetation sites frequently experience nutrient deficiencies, such as nitrogen and phosphorus, and impaired physiological processes due to the high pH and soil salinity. In this study, we examined the effects of nitrogen and phosphorus supplements in the NST-amended reclamation soils on growth and physiological parameters of trembling aspen (Populus tremuloides) and white spruce (Picea glauca) seedlings. We found that the growth and physiological responses of seedlings were superior in the mixture of NST and FMM compared with NST and PMM. Phytotoxicity of NST was associated with elevated boron levels. Trembling aspen exhibited greater sensitivity to NST but showed stronger growth improvements with increased nitrogen and phosphorus supplementation compared to white spruce. High levels of nitrogen and phosphorus supplementation alleviated the adverse effects on both species that were caused by mineral nutrient imbalance.
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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) | 0.001 | 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 | 0.001 |
| 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".