Microbial endophytes and compost improve plant growth in two contrasting types of hard rock mining waste
Bibliographic record
Abstract
The re-vegetation of mining wastes with native plants is a comparatively low-cost solution for mine reclamation. However, re-vegetation fails when extreme pH values, low organic matter, or high concentrations of phytotoxic elements inhibit plant establishment and growth. Our aim was to determine whether the combined addition of municipal waste compost and diazotrophic endophytes (i.e., microorganisms that fix atmospheric N2 and live within plants) could improve plant growth, organic matter accumulation, and phytostabilization of trace element contaminants in two types of hard rock mine waste. We grew a widespread native perennial grass, Bouteloua curtipendula, for one month in alkaline waste rock (porphyry copper mine) and tailings (Ag-Pb-Au mine, amended with dolomite) sourced from southeastern Arizona, United States. B. curtipendula tolerated elevated concentrations of multiple phytotoxic trace elements in the tailings (Mn, Pb, Zn), stabilizing them in roots without foliar translocation. Adding compost and endophyte seed coats improved plant growth, microbial biomass, and organic matter accumulation despite stark differences in the geochemical and physical characteristics of the mining wastes. The widespread grass B. curtipendula is a potential candidate for re-vegetating mine wastes when seeded with soil additives to increase pH and with microbial and organic amendments to increase 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 | 0.000 | 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".