Phytoremediation of Alberta oil sand tailings using native plants and a fungal endophyte species
Bibliographic record
Abstract
The Athabasca Oil Sands produce a high volume byproduct called tailing sands (TS). Typically, TS are remediated by planting young trees in large quantities of mulch (from elsewhere) plus mineral fertilizer. This is costly and labour intensive. Fungal endophytes colonize host plants without causing disease. Some endophytes confer plant tolerance to harsh environments. Trichoderma harzianum strain TSTh20-1 was isolated from a plant growing on Athabasca oil tailings sand (TS). TS are a high volume waste product from oil sand extraction that the industry is required to remediate. TS are low in organic carbon and mineral nutrients, and are hydrophobic due to residual hydrocarbons. In greenhouse trials, TSTh20-1 supports growth of tomato seedlings on TS without fertilizer. TSTh20-1 is a promising candidate for economical TS remediation. We tested 23 plant species for seed germination and growth on TS in the presence of TSTh20-1. The four best candidates are currently being used in microcosm-scale growth trials, and for outdoor mesocosm trials this summer. Potential mechanisms that contribute to endophyte-induced plant growth promotion are also being assessed. TSTh20-1 is nutritionally frugal, which may be characteristic of other plant fungal endophytes. We are also tagging TSTh20-1 with GFP to follow it in the plant and in the environment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".