Evaluating Alternate Post-Mining Land-Uses: A Review
Bibliographic record
Abstract
The ultimate objective of post-mine land-use and reclamation planning is to identify appropriate alternate land uses to which mined land could be put. This will ensure that land-use and morphology of the location will be capable of supporting either the prior land-use or pre-mining environment. The main challenge is usually, the choice of variables that must be considered in deciding a particular post-mining land-use. Literature reviews were conducted to identify the major factors needed to be considered in the selection of a post-mining land-use. This paper also looks at the most commonly practiced and accepted post-mining land-use techniques. Factors identified as important in the selection process include land resources (e.g. physical, biological and cultural characteristics), ownership, type of mining activity, legal requirements, location, needs of the community, economic, environmental, technical and social factors. In a broad categorization, all post-mining land-uses could be placed under one of the following land-use: agriculture, forestry, lake or pool, intensive recreational land-use, non-intensive recreational land-use, conservation and pit backfilling. However, the objective of any particular post-mining land use should be achieving economic and sustainable outcomes which meet human wants and needs, and protect life and the environment.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".