In vitro selection of root-associated fungi for use in ecological engineering and ecosystem restoration of iron ore waste rocks in Arctic and alpine tundra of Northern Quebec, Canada
Bibliographic record
Abstract
Mining activities typically involve the removal of plants, soil nutrients, and root-associated microorganisms, which in turn limits natural revegetation and soil restoration. Ecological restoration of mining-impacted sites using beneficial microorganisms, such as mycorrhizal fungi, is considered necessary and useful for the growth and survival of their host plants, but also for their tolerance to poor and contaminated environments, such as mine tailings. Here, nine fungal symbionts associated with plant roots from the Schefferville mine site in northern Quebec were tested for tolerance to overburden, waste rock and pure iron treatments. The production of ergosterol and the exudation of low-molecular-mass organic acids (citrate, malate, succinate, acetate) were measured by high-performance liquid chromatography during an in vitro experiment in liquid medium. These two types of metabolites are indicative of fungal growth and their amount varied in response to iron treatments. The study revealed that the cultivable root-associated fungi did not all respond in the same way to the abiotic stress applied. The results showed that the ericoid fungus Rhizoscyphus ericae exhibited the best growth in the presence of iron treatments. The dark septate endophyte Phialocephala fortinii ranked second in growth, but it produced the highest amount of organic acids. Cadophora finlandica and Hyaloscypha bicolor also showed good tolerance to iron treatments. These fungal isolates with the best growth and highest organic acid production are considered to be the most tolerant species to mine tailings and could potentially improve the survival, growth, and resistance of plant seedlings for mine reclamation in the Boreal Shield.
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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 | 0.001 | 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".