Quantitative methods for evaluating compaction in mine reclamation : A review and case study
Bibliographic record
Abstract
Compaction of growing media is commonly cited as a barrier to recovery in mine reclamation, and best practices include a variety of methods to avoid and mitigate compaction. Many British Columbia Mines Act permits include a clause that requires mines to “conduct research to assess decompaction methodologies to ensure that the severity of compaction that exists prior to commencing reclamation activities is effectively addressed.” However, compaction is difficult to assess with respect to effects on vegetation establishment and growth, and is thus rarely measured quantitatively for this purpose. Yet quantitative compaction assessments are necessary to evaluate whether compaction is present and needs to be addressed through site preparation or decompaction prior to revegetation. This paper reviews measures and associated methods for monitoring compaction in reclamation, including bulk density, relative bulk density, and mechanical resistance, and presents a literature review of values that limit root growth to provide general guidelines for operational use. A case study applying these compaction measures to a research trial at a mine in Western Canada is presented, showing the effects of different material types, construction methods, and site preparation methods on compaction. Compaction monitoring results are related to first-year survival of planted seedlings.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".