Growing plants on oily, nutrient-poor soil using a native symbiotic fungus
Bibliographic record
Abstract
The roots of land plants associate with microbes, including fungal symbionts that can confer abiotic stress tolerance. Bitumen extraction following oil-sand surface mining in the Athabasca region of Alberta, Canada removes plant nutrients but leaves a petrochemical residue, making the coarse tailings (CT) hostile to both plants and microbes. We isolated an endophyte strain of the Ascomycete Trichoderma harzianum we call TSTh20-1 (hereafter, TSTh) from a dandelion that was naturally growing on CT. TSTh colonization allowed tomato, wheat, and remediation seed mixtures to germinate and their seedlings to flourish on CT without the use of fertilizer. Compared to control plants, TSTh increased germination speed, percent germination, and biomass accumulation. TSTh also improved plant water use efficiency and drought recovery. TSTh-colonized plants secreted twice the level of peroxidase into CT as did plants alone. Over two months, plants colonized with TSTh doubled the petrochemical mobilization from CT over plants alone, suggesting a peroxide-mediated mechanism for petrochemical degradation. TSTh grew on autoclaved CT, bitumen, and other petrochemicals as sole carbon sources. Further, TSTh is a micro-aerobe that could metabolize 13C-phenanthrene to 13CO2 in 0.5% oxygen. TSTh has excellent potential for contributing to revegetating and remediating petrochemical contamination.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".