Long‐term impacts of peat‐based soil amendments promote ecological recovery in a boreal forest mine site in northern Ontario, Canada
Bibliographic record
Abstract
The mining industry faces significant challenges in restoring mine till to support the recovery of native ecosystems. This study evaluates the long‐term effects of peat‐based and non‐peat soil amendments on vegetation recovery at a boreal forest mine site in northern Ontario, Canada. Using a randomized block design with five replicates, eight soil amendment treatments were applied: Control, Fertilizer, Oats, Peat, Biosolids, Oats + Fertilizer, Peat + Fertilizer, and Peat + Biosolids. Four native woody species—Jack Pine (Pinus banksiana), Prairie Willow (Salix humilis), White Spruce (Picea glauca), and Speckled Alder (Alnus crispa)—were assessed for survival, height growth, and biodiversity recovery. Data were collected annually over 8 years (2016–2023), with statistical analyses conducted using Cox mixed‐effects models for survival, generalized linear mixed models for height, and linear mixed models for biodiversity indices such as species richness and the Shannon–Wiener index. Results indicate that peat‐based treatments, particularly Peat + Biosolids and Peat + Fertilizer, significantly improved woody species survival, height growth, and biodiversity compared to non‐peat treatments. Jack Pine demonstrated the highest growth, reaching a median height exceeding 200 cm by 2023, followed by White Spruce and Prairie Willow. Peat‐based treatments consistently supported greater species richness and Shannon–Wiener diversity indices over time, highlighting their effectiveness in ecological restoration. These findings provide practical insights for designing sustainable reclamation strategies in boreal forest ecosystems and underscore the critical role of peat‐based amendments in promoting vegetation recovery and biodiversity in reclaimed mine sites.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".