Tree establishment on post-mining waste soils: species, density, and mixture effects
Bibliographic record
Abstract
Tree establishment to restore degraded boreal post-mining lands is challenged by low soil productivity, a harsh microclimate, and potentially high contaminant levels. The use of mixed vegetation can facilitate the microclimate but increase competition for soil resources. A statistical accounting of plant–plant interactions and adaptation to multispecies conditions is hard to achieve in field experiments; trials under controlled conditions can distinguish effects of planting density and species interactions in the early stages of plant establishment. A greenhouse trial was established in containers (“mesocosms”) with waste rock or fine tailings from gold mines. Pregerminated (1-week-old) seedlings (Alnus viridis subsp. crispa, Picea glauca, Populus tremuloides, Salix arbusculoides) were planted using a Nelder density gradient design, modified for species combinations. A relative competition effect was estimated as a competitiveness index for each species combination, calculated as a ratio of α coefficients in the Holliday growth equation. The specific leaf area (SLA) was measured to indicate plant water stress adaptation. All species grew better in monoculture on fine tailings, while only P. tremuloides grew better in all mixtures on waste rock. Although net positive effects of density on SLA increment during early growth suggested microclimate improvement on fine tailings, no mixture provided advantages for both species in paired combinations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".