Effects of high‐carbon wood ash biochar on volunteer vegetation establishment and community composition on metal mine tailings
Bibliographic record
Abstract
Pyrolyzed organic waste, also known as biochar, is commonly used as a soil amendment and has recently been promoted to remediate metal mine tailings by increasing substrate pH, enhancing water and nutrient retention, and reducing bioavailability of toxic metals. Bottom ash from bioenergy facilities can contain high levels of charcoal residue, and thus qualify as a type of biochar according to international standards; the availability of this material at low costs makes it of particular interest in the context of tailings remediation. Naturally recruiting vegetation is critical in areas of primary succession such as mine tailings, and thus understanding vegetation responses on these substrates is essential. We examined responses of naturally regenerated “volunteer” vegetation to additions of high‐carbon wood ash biochar at a range of application rates (from 0 to 30 metric tons [t]/ha) at two gold mine tailings sites in northern Ontario, Canada over a 2‐year period. Volunteer vegetation cover increased with biochar dosage, peaking at 10–20 t/ha. Wood ash biochar amendments altered substrate physical properties (pH, electrical conductivity [EC], bulk density, total carbon [TC], and nitrogen [TN]), but effects varied by site and with dosage. Substrate TC and EC increased significantly with dosage at both sites with highest measures detected in the 10–20 t/ha amendment range. Species composition showed site‐ and dosage‐specific responses to biochar additions; however, species accumulation curves consistently showed peak species richness at intermediate dosages in both sites and both years of growth. Observed changes in volunteer vegetation suggest that low to moderate dosage applications of high‐carbon wood ash biochar can be highly beneficial for revegetation of mine tailings, but that wood ash impurities can result in deleterious effects at high dosages. Results from these field experiments demonstrate the potential of wood ash in tailings restoration and incentivizes additional in situ experiments to further discern site‐specific mechanisms.
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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 | 0.001 | 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".