Compost, plants and endophytes versus metal contamination: choice of a restoration strategy steers the microbiome in polymetallic mine waste
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Finding solutions for the remediation and restoration of abandoned mining areas is of great environmental importance as they pose a risk to ecosystem health. In this study, our aim was to determine how remediation strategies with (i) compost amendment, (ii) planting a metal-tolerant grass Bouteloua curtipendula, and (iii) its inoculation with beneficial endophytes influenced the microbiome of metal-contaminated tailings originating from the abandoned Blue Nose Mine, SE Arizona, near Patagonia (USA). We conducted an indoor microcosm experiment followed by a metataxonomic analysis of the mine tailings, compost, and root samples. Our results showed that each remediation strategy promoted a distinct pattern of microbial community structure in the mine tailings, which correlated with changes in their chemical properties. The combination of compost amendment and endophyte inoculation led to the highest prokaryotic diversity and total nitrogen and organic carbon, but also induced shifts in microbial community structure that significantly correlated with an enhanced potential for mobilization of Cu and Sb. Our findings show that soil health metrics (total nitrogen, organic carbon and pH) improved, and microbial community changed, due to organic matter input and endophyte inoculation, which enhanced metal leaching from the mine waste and potentially increased environmental risks posed by Cu and Sb. We further emphasize that because the initial choice of remediation strategy can significantly impact trace element mobility via modulation of both soil chemistry and microbial communities, site specific, bench-scale preliminary tests, as reported here, can help determine the potential risk of a chosen strategy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it