Life in mine tailings: microbial population structure across the bulk soil, rhizosphere, and roots of boreal species colonizing mine tailings in northwestern Québec
Bibliographic record
Abstract
Abstract Purpose Mining activities have negative effects on soil characteristics and can result in low pH, high heavy metal content, and limited levels of essential nutrients. A tailings storage area located in northwestern Québec showed natural colonization by plants from the adjacent natural environment. The objective of the study was to determine the main edaphic parameters that structured microbial populations associated with the indigenous woody plants that had naturally colonized the site. Methods Microbial populations were studied in the bulk soil, the rhizosphere, and inside plant roots using Illumina sequencing, ordination analysis (i.e., redundancy analysis (RDA) and principal coordinates analysis (PCoA)), ternary plotting, and statistical analysis (MANOVA). Results The main variables that drove the microbial community patterns were plant species and the tailings pH. Indeed, the main bacterial classes were Gammaproteobacteria and Deltaproteobacteria in both the rhizosphere and root endosphere. Analysis revealed that some dominant operational taxonomic units (e.g., Pseudomonas sp., Acinetobacter sp., and Delftia sp.) were present in increased proportions in roots for each plant species under study. This study also revealed that many of the most abundant fungal genera (e.g., Claussenomyces, Eupenicillium, and Trichoderma) were more abundant in the rhizosphere than in the root endosphere. Conclusions This comprehensive study of the microbial community dynamics in the bulk soil, rhizosphere, and root endosphere of boreal trees and shrubs could be beneficial in facilitating the rehabilitation of disturbed ecosystems.
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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 | 0.001 | 0.001 |
| 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".