Microbial communities from weathered outcrops of a sulfide-rich ultramafic intrusion, and implications for mine waste management
Bibliographic record
Abstract
ABSTRACT The Duluth Complex, Northeastern Minnesota, contains sulfide-rich magmatic intrusions that, collectively, represent one of the world’s largest known economic deposits of copper, nickel, and platinum group elements (Cu-Ni-PGEs). Previous work showed that microbial communities associated with experimentally-weathered Duluth Complex waste rock and tailings were dominated by uncultivated taxa and other populations not typically associated with mine waste. However, those experiments were designed for kinetic testing and do not necessarily represent the conditions expected for reclaimed mine waste or long-term weathering in the environment. We therefore used 16S rRNA gene methods to characterize the microbial communities present on the surfaces of naturally-weathered and historically disturbed outcrops of Duluth Complex material, as well as a circumneutral seep draining a reclaimed blast pit. Rock surfaces were dominated by diverse uncultured Ktedonobacteria, Acetobacteria , and Actinobacteria while seeps were dominated by Proteobacteria , including Leptothrix spp. and Methylovulum spp. All samples had abundant algae and other phototrophs. These communities were distinct from previously-described microbial assemblages from experimentally-weathered Duluth Complex rocks, suggested different energy and nutrient resources in the reclaimed rocks, outcrops, and seeps. Sulfide mineral incubations performed with and without algae showed that photosynthetic microorganisms could have an inhibitory effect on some of the autotrophic populations from the site, resulting in slightly lower sulfate release and differences in the dominant microorganisms. The microbial assemblages from these weathered outcrops show how communities are expected to develop during natural weathering of sulfide-rich Duluth Complex rocks, and represent baseline data that could be used to evaluate the effectiveness of future reclamation of tailings and waste rock produced by large scale mining operations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".