In vitro selection of ecologically adapted ectomycorrhizal fungi through production of fungal biomass and metabolites for use in reclamation of biotite mine tailings
Bibliographic record
Abstract
Mineral weathering plays an important role in poor-nutrient environments such as mine spoils and tailings. Ectomycorrhizal (ECM) fungi are able to enhance mineral weathering through different mechanisms, thereby increasing the availability of minerals and nutrients to plants. Six ECM fungi (Cadophora finlandia, Cenococcum geophilum, Hebeloma crustuliniforme, Lactarius aurantiosordidus, Paxillus involutes, and Tricholoma scalpturatum) were tested here for their tolerance to biotite-quartz-rich mine tailings. Either solid- or liquid-medium methods were used for in vitro selection of ECM fungi for their ability to grow on mine tailings. ECM fungi were selected based on their mycelial radial growth and metabolite production (ergosterol and low-molecular-mass organic acids, LMMOAs). We found a strong correlation between fungal ergosterol content and mycelial radial growth using the solid-medium method. However, the liquid-medium method was more appropriate for ergosterol synthesis and permitted direct measurement of organic acid production. We found that LMMOAs were exuded by ECM fungi, which solubilized mine tailings for their own growth and nutrition. Finally, we concluded that the ECM fungi C. finlandia and T. scalpturatum are the species most tolerant to tailings and could potentially improve the survival rate, growth, and health of white spruce seedlings planted on biotite mine spoils and tailings.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".