Uncertainty-based mine planning framework for oil sands production scheduling and waste management
Bibliographic record
Abstract
In oil sands mining, the production schedule must be integrated simultaneously with in-pit and ex-pit dike construction scheduling. Any extra mined ore is stockpiled for a limited duration, and the topmost layer of the overburden is used for land reclamation. An uncertainty-based mathematical programming model is developed based on mixed integer linear goal programming for oil sands production scheduling and waste management. The model aims to maximize the net present value (NPV) while meeting all required production and technical constraints. The reclamation strategy for the stockpiled ore and the destination of dike materials is determined to minimize costs. The model uses kriged estimates with a variance penalty scheme to minimize the financial risk from grade uncertainty associated with the production schedule. The uncertainty-based model is implemented for an oil sands mine case study with two scenarios. An integrated mine plan with a waste management and stockpiling strategy is generated by Scenario 1 that maximizes the NPV of the operation and minimizes dike construction and reclamation cost. Scenario 2 uses the variance penalty scheme to estimate the production schedule financial risk from grade uncertainty.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".