Biochar for Restoration in the Boreal Forest of Northwestern Ontario, Canada
Bibliographic record
Abstract
Pyrolyzed organic material, or biochar, has been proposed as an effective soil amendment for reforestation and mine reclamation due to its capacity to retain soil nutrients and water, immobilize toxic metals, and its recalcitrance to mineralization. Potential sources of biochar in remote northern sites are high-carbon wood ash from wood waste gasification, and natural charcoals generated by wildfire. My thesis examines the effects of biochar application on boreal species growth and establishment in three study systems: (1) the effects of a poplar wood biochar and a mixed-feedstock high-carbon wood ash biochar on soil and vegetation in a 3-year field experiment in NW Ontario, Canada; (2) the effects of a ‘natural’ biochar and mixed-feedstock high-carbon wood ash biochar on mine tailings reclamation in a 2-year field experiment at a gold mine in northern Ontario, Canada; and, (3) the potential for biochars to sorb allelochemicals and mitigate allelopathic effects on seed germination and early seedling development in three common invasive plants in Canada, garlic mustard (Alliaria petiolata), tree of heaven (Ailanthus altissima) and yellow sweetgrass (Melilotus officinalis). I found that biochars improved soil conditions and enhanced growth of native plant species, resulting in large increases in substrate C, reduced substrate bulk density, and up to a 4.5-fold increase in planted native grass biomass. Biochar additions also enhanced availability of the plant nutrients P, K, Mg, and Zn; high-carbon wood ash biochar increased spot measurements of soil moisture, but also increased levels of Cu, Mn, and Pb. Natural biochar and mixed-feedstock high-carbon wood ash biochar reduced cover of Melilotus spp., an abundant invasive and allelopathic taxon. Amendments had large effects on plant community composition, generally favoring native species. Addition of high-carbon wood ash resulted in declines in growth performance of planted white spruce (Picea glauca); a path analysis suggests this was due to effects of toxic elements rather than indirect effects of competition with non-tree vegetation. I conclude that while there are large differences in plant species responses to biochars, and potential for toxicity effects and indirect effects mediated by plant competition, overall, results suggest some benefits of biochars, including high-carbon wood ash biochars, for forest regeneration and mine reclamation in northern environments.
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".