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Record W6902882331 · doi:10.7939/r3-tm8z-hf29

Growth and physiology of mycorrhizal plants in soil containing enhanced non-segregating oil sands tailings

2024· dissertation· en· W6902882331 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2024
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsTailingsOil sandsLand reclamationPeatSoil waterChlorophyllNutrientPhotosynthesis

Abstract

fetched live from OpenAlex

Open-pit mining of oil sands in Alberta, Canada, has resulted in a large-scale land disturbance of the boreal forest ecosystem and produced large amounts of tailings. To improve the effectiveness of the tailings solidification process and to make the tailings less harmful to vegetation, the novel oil sands tailings management technologies of the enhanced non-segregating tailings (eNST) and enhanced-spiked non-segregating tailings (esNST) have been developed. However, the full impact of these tailings on vegetation remains to be determined. Most plant species form symbiotic associations with fungi, which benefit plants, especially in poor-quality soils and under stressful conditions. The effects of eNST and esNST on the growth of mycorrhizal plants used for oil sands reclamation are little known. Therefore, in my thesis research, I carried out two studies to examine the growth and physiology of mycorrhizal plants growing in soil containing eNST and esNST. In the first study, velvetleaf blueberry (Vaccinium myrtilloides) and Labrador tea (Ledum groenlandicum) were inoculated with Oidiodendron maius and Pezoloma ericae and grown in the peat mineral mix (PMM) that is commonly used as the reclamation soil, PMM/eNST (1:1, by volume), and PMM/esNST (1:1, by volume) for eight weeks. Then, the growth parameters, gas exchange, and tissue concentrations of chlorophyll and elements were measured in plants. The addition of eNST and esNST to PMM significantly increased the pH of the growth substrate and reduced its nutrient concentrations and water holding capacity. After eight weeks of treatments, both eNST and esNST significantly reduced velvetleaf blueberry and Labrador tea growth, reduced chlorophyll concentrations, net photosynthesis and transpiration rates, and increased leaf Na concentrations. Inoculation with the P. ericae and O. maius notably improved plant responses to tailings, with P. ericae showing more pronounced benefits. Compared with non-inoculated plants, mycorrhizal plants had lower leaf Na and B concentrations, higher P concentrations, as well as net photosynthesis rates, and dry weights, especially when exposed to esNST. In the second study, jack pine (Pinus banksiana) and white spruce (Picea glauca) were inoculated with Laccaria bicolor and subjected to PMM (control), PMM/eNST (1:1, by volume), PMM/esNST (1:1, by volume), eNST, and esNST treatments for eight weeks. The growth of jack pine and white spruce was severely impaired by eNST and esNST, with significant reductions in chlorophyll concentration, net photosynthesis, and total dry weights. This may be partly attributed to imbalanced nutrient absorption due to high Na concentrations in the leaves. Jack pine was more sensitive to tailings compared with white spruce and exhibited greater growth reductions compared with white spruce. However, the addition of PMM significantly increased the concentrations of K and Ca in jack pine and white spruce needles, and the improved nutrient balance likely contributed to the alleviation of tailings effects on the physiology and growth of plants. Inoculation with L. bicolor further enhanced plant tolerance to tailings, reduced needle B concentrations in jack pine seedlings treated with eNST, and needle B and Na concentrations in white spruce seedlings treated with eNST and esNST. The improvement of physiology and growth of plants exposed to eNST and esNST by L. bicolor demonstrates the importance of mycorrhizal associations for plants in reclamation areas affected by oil sands tailings and their potential for improving revegetation efforts.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.004
GPT teacher head0.172
Teacher spread0.168 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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