Forest restoration following surface mining disturbance: challenges and solutions
Bibliographic record
Abstract
Many forested landscapes around the world are severely altered during mining for their rich mineral and energy reserves. Herein we provide an overview of the challenges inherent in efforts to restore mined landscapes to functioning forest ecosystems and present a synthesis of recent progress using examples from North America, Europe and Australia. We end with recommendations for further elaboration of the Forestry Reclamation Approach emphasizing: (1) Landform reconstruction modelled on natural systems and creation of topographic heterogeneity at a variety of scales; (2) Use and placement of overburden, capping materials and organic amendments to facilitate soil development processes and create a suitable rooting medium for trees; (3) Alignment of landform, topography, overburden, soil and tree species to create a diversity of target ecosystem types; (4) Combining optimization of stock type and planting techniques with early planting of a diversity of tree species; (5) Encouraging natural regeneration as much as possible; (6) Utilizing direct placement of forest floor material combined with seeding of native species to rapidly re-establish native forest understory vegetation; (7) Selective on-going management to encourage development along the desired successional trajectory. Successful restoration of forest ecosystems after severe mining disturbance will be facilitated by a regulatory framework that acknowledges and accepts variation in objectives and outcomes.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".