High-carbon wood ash biochar for restoration of metal mine tailings
Bibliographic record
Abstract
Canada’s history of unregulated mining activities has left a legacy of orphaned, barren mine tailings areas exposed and unmanaged, posing structural and chemical risks, while modern operational mines contribute an ever-increasing volume of tailings stored on site. Re-establishing vegetation to ensure that tailings structures are self-sustaining, coherent with surrounding ecology, and chemically secure remains a critical challenge. Bottom ash from bioenergy facilities is currently landfilled in Canada, yet represents a potential nutrient-rich, alkaline soil amendment. Bottom ash can contain high levels of chemically recalcitrant charcoal residues, and thus qualify as a type of biochar for use on soils; its broad availability and low cost makes it appealing for tailings restoration. This thesis investigates recycling of high-carbon wood ash biochar (HCWAB) for emulating wildfire residues on disturbed mining land to promote ecosystem recovery. Through multi-year field studies on both historic (exposed) and modern (sand-capped) metal mine tailings in Canada’s boreal forest, I found that: (1) low to moderate dosage (3-13 t/ha) applications of HCWAB can be highly beneficial for encouraging volunteer vegetation, but that wood ash impurities can result in deleterious effects at high dosages; (2) volunteer plant species composition displays site- and dosage-specific responses to HCWAB additions; however, greatest species richness is observed at intermediate dosages; (3) the survival and growth of native saplings and transplanted wild trees (“wildings”) peaks at mid-range HCWAB dosages; and (4) tree tissue concentrations and substrate availability of potentially toxic metals from tailings and HCWAB remain below levels of toxicity concern across both sites; however, metals concentrations increase with high dosages of HCWAB (30 t/ha) at the historic, more initially contaminated site. Higher HCWAB application dosages at the sand-capped site are also associated with increased herbaceous volunteer cover and planted tree performance is negatively correlated with vegetation cover, consistent with a resource competition effect. Methodologically, I introduce laser-ablation ICP-MS for conifer tissue analysis, making possible trace metal estimates for small shoot extension growth samples typical of northern boreal forests. In sum, HCWAB holds great potential for restoration of both historic, exposed tailings and modern, sand-capped tailings; future field operational trials are advocated, focusing on strategies to refine site-specific dosages, reduce competition effects, and explore possible co-amendment strategies.
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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".