Effects of Non-segregated Tailings, Nitrogen, Phosphorus and Elemental Sulphur on Growth of Plants in Oil Sands Reclamation Soils
Bibliographic record
Abstract
Oil sands mining in northeastern Alberta, Canada, has disturbed large areas of the northern boreal forest. These disturbed areas must be reclaimed to forest ecosystems with equivalent land capability after mining closure. The extraction of bitumen is carried out with recycled hot water containing NaOH and, as a result, the oil sands tailings have high pH and elevated levels of Na+, which are harmful to plants. To accelerate tailings consolidation, other chemicals may be added, which further affect tailings chemistry and can potentially contribute to their phytotoxicity. To alleviate this concern, Canadian Natural Resources Limited (CNRL) has developed novel tailings technologies to consolidate fine tailings and produce non-segregated tailings (NST) using thickeners in combination with CO2. However, NST may still have negative effects on plants. During oil sands reclamation, a layer of forest mineral soil mix (FMM), that is salvaged from upland boreal forest sites, or peat mineral mix (PMM), that is stripped from the peatlands, are placed on the top of overburden materials and coarse tailings sands before revegetation. The pH of the topsoil may increase due to the high pH of the underlying layers and affect the revegetation efforts. In the present thesis project, two studies have been conducted to address some of the above concerns. In the first study, I examined the effects of soil pH and elemental sulphur on growth and physiological parameters in Saskatoon (Amelanchier alnifolia) and beaked hazelnut (Corylus cornuta) seedlings. I found that elemental sulphur was effective in lowering soil pH. However, the addition of elemental sulphur to the pH 5.7 lowered the soil pH to very low levels and impaired growth and physiological performance of Saskatoon and beaked hazelnut plants. Saskatoon and beaked hazelnut seedlings growing in the soil of pH 8.5 did not substantially benefit from the addition of 5 and 25 g kg-1 elemental sulphur to the soil. The results demonstrated that 5 g kg-1 was not sufficient to lower soil pH to the desirable neutral to slightly acidic level, and 25 g kg-1 was too high and resulted in excessive soil acidity. In the second study, I examined the effects of nitrogen and phosphorus levels in the NST-affected soil on growth and physiological parameters of trembling aspen (Populus tremuloides) and white spruce (Picea glauca) seedlings. I found that the growth and physiological responses of seedlings were increased in the mixture of NST and FMM compared with NST and PMM. Trembling aspen was more affected by NST than white spruce and benefitted more from the higher N and P soil levels. The results of both studies may be helpful in addressing the concerns of high pH and improve oil sands reclamation efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".