Design and field experimentation of a robotic system for tailings characterization
Bibliographic record
Abstract
There is an ongoing requirement to conduct ground surveys of engineered mine tailings deposits to monitor dewatering performance and consolidation prior to completing reclamation work. The deposit variability can make such surveys hazardous for humans. A rover is described that has been developed and deployed for characterizing reclaimed soil regions. This paper presents the functional requirements for unmanned ground vehicles used in this application, including the need for low-risk and timely subsurface sampling and terrain parameter estimations on highly uncertain terrains. Developments of the field-ready prototype wheeled rover are summarized, including tooling; and field tests are described in an industrial site at an Athabasca oil sands facility. Experiments on tailings treatment cells showed the feasibility of the sampling technologies and parameter estimation methods based on classical terramechanics models. The rover capabilities were further demonstrated by collecting samples from production treatment cells and estimating the cohesion and internal friction angle of tailings sand used in fluid containment dykes. The limitations of the current system helped identify future work for the design and development of new mobile robot systems for 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".