Bacterial diversity and community structure in native legume root nodules in intact and mining-disturbed Arctic tundra
Bibliographic record
Abstract
Mining restoration in Arctic regions is challenging due to harsh environmental conditions with slow natural ecosystem recovery taking decades or even centuries. Identifying native species that can both colonize disturbed areas and contribute to soil development is critical to hasten restoration timelines. Nodules were collected from four native legume species ( Astragalus alpinus Linnaeus, Hedysarum americanum (Michaux ex Pursh) Britton, Oxytropis arctica R. Brown, and Oxytropis maydelliana Trautvetter) observed to be naturally colonizing gravel quarries within a mine footprint near Rankin Inlet, Nunavut, Canada. Samples came from both gravel quarries and adjacent intact tundra . Next-generation sequencing of the 16S and nifH regions was used to characterize the nodule bacterial community composition and diversity. Despite the large differences in soil conditions between gravel substrates and intact tundra, no significant effects of soil environment were found on bacterial community composition within plant nodules. Further, few differences were observed in nodule communities between the plant species. Overall, the study suggests that the microbial propagules necessary for successful nodulation are present in gravel quarries. While restoration efforts involving native legumes may succeed without commercial inoculants, further research is needed to determine whether the rhizobia in these environments can provide sufficient nitrogen to support robust host plant growth.
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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 | 0.000 |
| 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".