Forced Orientation of Graphs
Bibliographic record
Abstract
The concept of forced orientation of graphs was introduced by G. Chartrand et al. in 1994. If, for a given assignment of directions to a subset S of the edges of a graph G, there exists an orientation of E(G)nS, so that the resulting graph is strongly connected, then that given assignment is said to be extendible to a strong orientation of G. The forced strong orientation number f D (G), with respect to a strong orientation D of G, is the smallest cardinality among the subsets of E(G) to which the assignment of orientations from D, can be uniquely extended to E. We use the term defining set instead of "forced orientation" to be consistent with similar concepts in other combinatorial objects. It is shown that any minimal strong orientation defining set is also smallest. We also study Spec(G), the spectrum of G, as the set of all possible values for f D (G), where D is taken over all strong orientations of G. Key words: Forced orientation, defining set, matroid, strong orientation, unique extension 1
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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".