A combined compost, dolomite, and endophyte addition is more effective than single amendments for improving phytorestoration of metal contaminated mine tailings
Bibliographic record
Abstract
Abstract Background and aims Re-vegetation of mining-impacted landscapes reduces transport of toxic elements while improving soil fertility. This study evaluated whether the planting of a native perennial grass with a consortium of diazotrophic microbial endophytes and municipal waste compost—alone and in combination—enhanced plant growth while stabilizing metal(loids) in dolomite-amended tailings from a historically mined polymetallic mineral deposit. Methods We grew Bouteloua curtipendula seedlings in tailings with hazardous concentrations of As, Cd, Pb, Mn, and Zn. We evaluated how plant growth, organic matter accumulation, and major, minor, and trace element mobilization and phytostabilization responded to microbial endophyte and/or compost amendments after the 45-day growth experiment. Results Although most of the added endophytes were not uniquely identified, the best plant growth and fertility outcomes were achieved with a combination of amendments: dolomite to reduce acidity, compost to increase nitrogen, and a mixed consortium endophyte seed coating to synergistically increase organic carbon and grass biomass yields. Compost reduced shoot and root concentrations—but not yields—of contaminant metals. Endophytes increased foliar Cd, Co, Mn, and Pb yields but mobilized Pb and Zn from the tailings. Root stabilization of Cd, Co, Mn did not require amendments. Conclusion The most effective means of revegetating these acidic, polymetallic tailings with the native B. curtipendula is with a simultaneous dolomite, compost, and endophyte seed treatment. Due to potential phosphate solubilization and siderophore production by this consortium of endophytes, strategies to capture solubilized metal(loids) may be needed for sulfidic tailings with metal(loids) associated with mobile mineral phases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".