Mycorrhizae and Rhizobacteria on Precambrian Rocky Gold Mine Tailings: I. Mine-Adapted Symbionts Promote White Spruce Health and Growth
Bibliographic record
Abstract
White spruce (Picea glauca (Moench) Voss) is a commercially valuable boreal tree that has been known for its ability to colonize deglaciated rock tailings. Over the last decade, there has been an increasing interest in using this species for the revegetation and successful restoration of abandoned mine spoils. Herein, we conducted a glasshouse experiment to screen mycorrhizal fungi and rhizobacteria capable of improving the health and growth of white spruce seedlings growing directly on waste rocks or fine tailings from the Sigma-Lamaque gold mine located in the Canadian Abitibi region. After 32 weeks, measurements of health, growth and mycorrhizal colonization variables of seedlings were performed. Overall, symbionts isolated from roots of healthy white spruce seedlings growing on the mining site, especially Cadophora finlandia Cad. fin. MBN0213 Genbank #KC840625 and Pseudomonas putida MBN0213 GenBank #AY391278, were more efficient in enhancing seedling health and growth than allochthonous species and constitute promising microbial symbionts. In general, mycorrhizae promoted plant health and belowground development, while rhizobacteria enhanced aboveground plant biomass. The observed beneficial effects were substrate-, strain-, and/or strains combination- specific. Therefore, preliminary experiments in control conditions such as the one described here can be part of an efficient and integrated strategy to select ecologically well adapted symbiotic microorganisms, critical for the success of a long-term revegetation program.
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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".