Insights on the response of microbial communities to vegetation and biogeochemical properties in amended and unamended acid-generating mine wastes
Bibliographic record
Abstract
Understanding the structure of microbial communities in acidic, metal contaminated tailings and waste rock in cool, continental northern temperate climates is important for identifying their potential for use in phytostabilization and bioremediation programs. In this study, microbial community compositions across two large mine waste sites in the Sudbury basin were analyzed along gradients of barren-to-vegetated tailings and waste rock. Microbial communities were less diverse, more homogenous, and mainly chemolithoautotrophic in barren sites, transitioning to more diverse communities of organoheterotrophs, nitrogen-fixing/plant-growth promoting bacteria, and ectomycorrhizal fungi in the vegetated areas. Network interaction analyses of microbial communities demonstrated higher levels of interactions of microbial groups in the barren tailings, which were related to the extreme environmental and chemical conditions and restricted anabolic nutrient availability of the substrates. We also show that vegetation and plant-derived organic matter in the tailings are associated with the shifts observed in the structure of microbial communities towards more soil-like communities, which sequentially further aids in the establishment of vegetation. These findings have implications for the optimization of phytoestablishment and bioremediation in mine tailings and waste rock in the cool, continental northern temperate and boreal climates.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".