Evaluating the potential of alder-Frankia symbionts for the remediation and revegetation of oil sands tailings
Bibliographic record
Abstract
Tailings are the waste produced as a result of the extraction of oil from the tar sands in northern Alberta. Many avenues for the reclamation of tailings are being researched, but one area that has received little attention is phytoremediation. The Alder-Frankia symbiotic relationship in the tailings was investigated for its potential in revegetation and remediation of the tailings. Two species of alders were examined Alnus glutinosa and A. rugosa. The impact of the alders was monitored through the investigation of the differences in the microbial community present in the oil sands tailings and composite tailings (CT) with and without alders. For our investigation we used culture dependent techniques (plate counts and mineralization assays) and culture independent techniques (16S rRNA gene PCR, catabolic PCR and DGGE). The alders lowered the pH of the tailings, increased rates of mineralization, increased the general microbial population in the tailings by one to two orders of magnitude and increased the microbial diversity. A. rugosa however, had a greater impact on the mineralization of poly aromatic hydrocarbons (PAHs) and, being native to Alberta, was chosen for further experimentation, using only composite tailings. The aim of the experiments was to determine the effect of a Frankia inoculum on the growth of A. rugosa in (CT) and the associated microbial community. The microflora in the bulk soil, rhizosphere and inside the root of inoculated and non-inoculated A. rugosa were compared through microbial enumerations of the community, with general and selective media and mineralization assays. A. rugosa inoculated with Frankia was taller and the roots were more developed and the endophytic community of inoculated A. rugosa had greater rates of naphthalene mineralization. The results indicate that A. rugosa inoculated with Frankia could be used for the phytoremediation of tailings and for the re-establishment of a forest ecosystem.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".