Parametric Simulation of Shovel-Oil Sands Interactions During Excavation
Bibliographic record
Abstract
Hydraulic shovel excavators are widely used as primary production equipment in surface mining for removing overburden and ore materials. Variability in material diggability, unstructured mining environments and limited space, effective machine operation, and machine logistics affect the performance of the hydraulic shovel excavators. A hydraulic shovel simulator is developed to simulate the performance of hydraulic shovels for oil sands extraction in the ADAMS simulation environment. The shovel-oil sands interaction is simulated using a reformulated universal earth-moving model. The simulated digging parameters include the bucket dynamics, oil sands properties, oil sands-bucket interactions, and operating variables. The results show that the simulator is capable for identifying the parameters, which influence the performance of hydraulic shovels. This parameterized simulator provides a powerful tool for performance monitoring, excavation process designs and structural optimization of hydraulic excavators. The method presented in this paper forms the basis for developing comprehensive simulator models for automated shovel operations in constrained mining environments.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".