Novel approaches of geotechnical investigation for mine closure projects in Canada
Bibliographic record
Abstract
Innovative data acquisition and ground monitoring approaches are now being used to support the decommissioning and reclamation of the various types of excavations found at mine sites. Remote data acquisition and visualization technologies have improved the ability to gain a spatial understanding of mine excavations and structures as well as the quality of the surrounding rock mass. These technologies have led to greater confidence and reliability in the outcome of mine closure designs by enabling advanced engineering analyses and increasing personnel safety. To ensure the safety of the public and allow for productive use of the land after the mine is closed, geotechnical assessments are conducted to evaluate the longer-term stability of open pit and underground mine excavations. The acquisition of data to support these studies can be challenging particularly for historic or legacy mine sites where there is limited availability of design and implementation records and inspection reports on the state of excavations post mining. These excavations can also be difficult to access safely because of rock mass damage caused by instability surrounding these structures. The installation of ground monitoring methods can also be challenging due to the instability that occurs post-mining. The technologies and monitoring methods discussed in this paper include photogrammetry, slope inclinometers (SI), time domain reflectometry (TDR), bathymetric and sonar surveys, terrestrial, Light Detection and Ranging (LiDAR), and Interferometric Synthetic Aperture Radar (InSAR). Example uses of these technologies and surveying methods in various mine closure projects are reviewed including advantages and limitations of each technique mentioned above. Assessing the usage and execution of these techniques can determine which methods are appropriate for use in other projects.
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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 | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".