Mapping mining waste : an investigation of the impact of mining activities on settlement patterns
Bibliographic record
Abstract
This research focuses on the waste and environmental damage caused by mining activities and the impact that this has on settlement pattern of adjacent areas. This research identifies that mining cities are unique in their land use dynamics due to the physical attributes of mining sites and there impacts on human and nature. Using a method of three sequential approaches to understand the land use dynamics of mining cities the first study examines the physical attributes of mining sites, through the creation of a new data set that combines existing and abandoned sites from existing separate datasets, outlining production, proximity to settlement areas and water bodies to identify their degree of threat to human and nature. Secondly, a single case study of Copper Cliff, ON is used to investigate how mining activities and its changes interact with surrounding land uses through a Land mosaic-function-land change feedback model adapted from Richard T.T. Forman’s theory of land mosaic. The analysis then investigates the policy responses that are enacted to mitigate the mining activities with other land uses. The analysis identifies that the potential impact of mining activities is more prominent where mining waste production is higher and located at close proximity to settlement areas. However, although the growth pattern of settlement areas are often guided by the physical characteristics of mining sites, effective response of land use policies may stimulate positive changes of land use pattern.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".