The application of in-situ technology in reclamation and decommissioning plans for tailings impoundments
Bibliographic record
Abstract
Prior to the decommissioning and reclamation of tailings impoundments it is critical to adequately characterize the geotechnical, hydrogeological, and geochemical nature of tailings. Important advances in the capability of in-situ testing for characterizing tailings impoundments have included the development of the resistivity piezocone and improved water sampling technologies. The standard piezocone is an excellent means of logging stratigraphy for most soils and also gives accurate estimates of key physical and hydrogeological parameters. The resistivity piezocone includes a geophysical module, which resides directly behind the standard piezocone during testing, which permits assessment of groundwater quality through the measurement of the bulk resistivity of the soil and the pore water. In addition, recently developed in-situ discrete groundwater sampling systems can be used to augment the bulk resistivity measurements and facilitate site-specific correlations with selected water quality parameters. This paper presents an overview of the resistivity piezocone and improved water sampling technologies and provides a summary of field testing programs carried out at large sulphide tailings impoundments at three Canadian mines. Test results are presented with respect to environmental and geotechnical aspects of sulphide tailings characterization.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".