Sustainable management and rehabilitation of mine sites for decision support - remote sensing innovations and applications
Bibliographic record
Abstract
Waste byproducts of mining activities can have environmental, social, and economic impacts. In some cases, resulting elevated concentrations of heavy metals and acid-generating tailings leave an environmental footprint that requires long-term monitoring of remediation efforts to prevent or reduce the degradation of surrounding ecosystems. In other examples, the presence of toxic, and sometimes radioactive, wastes can pose immediate health risks to nearby communities through dust dispersal and surface-water and groundwater contamination, and longer-term danger with contaminant transport throughout the regional environment. There are an estimated 27 000 orphaned and abandoned mines across Canada, and billions of dollars of remediation liability for acid-mine drainage, disruption of critical habitats, and related socio-economic impacts. Since 1991, legislation in Canada requires the mining industry to supply detailed procedures for the long-term management of mine-waste sites. Information-extraction techniques exploiting Earth Observation and remote sensing data, in the form of software tools and processing methodologies, are discussed. The remote sensing data available for this study include airborne and satellite synthetic aperture radar (SAR) imagery, multispectral and broadband optical satellite imagery, and airborne and satellite hyperspectral imagery (imaging spectrometry), and in some cases multitemporal data sets are utilized. Several in situ data sets were collected to develop and validate the techniques. Sites discussed in this study include Seal Harbour, Nova Scotia; Britannia Beach, British Columbia; Key Lake, Saskatchewan; and Sudbury, Ontario.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".