Oil sands production scheduling and waste management with optimum cut-off grade policy
Bibliographic record
Abstract
Cut-off grade is the criterion that separates ore from waste and it determines the amount of ore and waste material in the final pit limit. To generate an optimum production schedule for the mine life, cut-off grade optimisation is used to determine the cut-off grade, duration of mining of the grade and the amount of material mined. This research developed a heuristic optimisation model that generates an optimum cut-off grade policy and a schedule for ore and waste material including overburden (OB), interburden (IB) and tailings coarse sand (TCS) dyke materials for dyke construction in oil sands mining. Scenarios investigated include: no stockpiling and stockpiling with limited reclamation duration. The scenario of reclaiming the stockpile after one year had the highest net present value (NPV) as well as the highest cut-off grade profile. Reclaiming the stockpile after two years had less NPV due to reduction in processing recovery resulting from oxidation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".