Digraph Description Of K-Interchange Technique For Optimization Over Permutations (Extended Abstract)
Bibliographic record
Abstract
) Jeffrey B. Sidney, University of Ottawa, Ottawa, K1N 6N5 Canada Email: sidney@admin.uotttawa.ca Mark Sh. Levin, The Research Inst., College of Judea & Samaria, Ariel, Israel Fax: 972-3-9366834, Email: mslevin@research.yosh.ac.il Abstract The paper describes a general glance to the use of element exchange technique for optimization over permutations. A multi-level description of problems is proposed which is a fundamental to understand nature and complexity of optimization problems over permutations (e.g., scheduling, traveling salesman problem). The description is based on permutation neighborhoods of several kinds (e.g., basic, by improvement of objective function). Issues of an analysis of problems and a design of heuristics are discussed. 1 Introduction For many years efforts of researchers in combinatorial optimization were oriented to the design of effective (polynomial) algorithms for problems on permutations. Scheduling problems and linear ordering problems are representativ...
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".