Revegetation and reclamation of oil sands process-affected material using «Frankia»-inocculated alders: field and greenhouse trials
Bibliographic record
Abstract
Canada's oil sand industry produces substantial quantities of oil sands process-affected material (OSPM) including composite tailings (CT) and tailings sands (TS) that need to be reclaimed on site. This work evaluated the establishment of a pioneer plant species, alder, inoculated with a nitrogen-fixing actinomycete, Frankia. The main objectives were to evaluate alder performance in OSPM and its impact on soil characteristics and microbial community structure and function. This project was divided into two phases: a field study and a greenhouse trial. The greenhouse trial tested 2 alder species, Alnus glutinosa and A. crispa, in CT and TS. In addition to Frankia inoculation, a tripartite association with a mycorrhizal fungus, Glomus intraradices was evaluated. The field study consisted of a 2 year monitoring of Frankia-inoculated alders (A. crispa (Ait.) Pursh.) planted in TS capped with overburden material and peat moss. The parameters tested were the following: plant biomass and nitrogen content; soil chemical characteristics; microbial biomass; microbial petroleum hydrocarbon mineralization capability; and microbial community diversity and composition using the molecular techniques, PCR and DGGE. Alders performed well in OSPM and Frankia inoculation improved biomass acquisition. Frankia-alders improved a number of soil quality parameters such as pH, sodium content, and CEC. Frankia-alders presence modified soil microbial activity and diversity. Alder rhizosphere sustained more microbial biomass than unplanted soil, whereas the tripartite association did not provide additional benefits. Overall, Frankia-inoculated alders are an interesting biotechnological approach for the reclamation and revegetation of oil sand process-affected materials. Abbreviation: PCR (polymerase chain reaction), DGGE (Denaturating Gradient Gel Electrophoresis), CEC (Cation Exchange Capacity), TS (Tailings Sands), CT (Composite
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".