Arc‐Disjoint In‐ and Out‐Branchings With the Same Root in Locally Semicomplete Digraphs
Bibliographic record
Abstract
Abstract Deciding whether a digraph contains a pair of arc‐disjoint in‐ and out‐branchings rooted at a specified vertex is a well‐known NP‐complete problem (as proved by Thomassen, see ). This problem has been shown to be polynomial time solvable for semicomplete digraphs and for quasi‐transitive digraphs . In this article, we study the problem for locally semicomplete digraphs. We characterize locally semicomplete digraphs that contain a pair of arc‐disjoint in‐ and out‐branchings rooted at a specified vertex. Our proofs are constructive and imply the existence of a polynomial time algorithm for finding the desired branchings when they exist. Our results generalizes those from for semicomplete digraphs and solves an open problem from .
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".