Bibliographic record
Abstract
Many aspects of open pit mine planning can be modelled as a combinatorial optimization problem. This thesis reviews some existing mine scheduling methods and some of their short comings. Many of the problems are related to the partially ordered knapsack problem with multiple knapsack constraints. This is a special case of a maximum directed cut problem with multiple knapsack constraints on the arcs in the cut.The major contribution of this thesis is the study of the directed cut polytopeand cone, which are the convex hull and positive hull of all directed cut vectors ofa complete directed graph, respectively. Many results are presented on the polyhedralstructure of these polyhedra. A relation between the directed cut polyhedraand undirected cut polyhedra is established that provides families of facet defininginequalities for the directed cut polyhedra from the undirected cut polyhedra.A polynomial time algorithm for optimizing over the undirected cut polytope isgiven for the special case of when an objective function has the same optimal valueon two relaxations, the rooted metric polytope and the metric polytope. Projectionsof the directed cut polytope onto the arc set of an arbitrary directed graph are researched.A method known as triangular elimination is extended from the undirectedcut context to a directed cut context. A complexity result proving that the problem of selecting a physically connected maximum value set of blocks from a 2D grid is NP-hard is given. In the mining literature such a grid would be called a bench.An implementation of a LP rounding algorithm known as pipage rounding isapplied to a pushback design problem. This simple and efficient technique producesresults within 6.4% of optimal for a real data set.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".