Bibliographic record
Abstract
Abstract The star chromatic index of a multigraph G, denoted , is the minimum number of colors needed to properly color the edges of G such that no path or cycle of length four is bicolored. A multigraph G is star k‐edge‐colorable if . Dvořák, Mohar, and Šámal [Star chromatic index, J. Graph Theory 72 (2013), 313–326] proved that every subcubic multigraph is star 7‐edge‐colorable. They conjectured in the same article that every subcubic multigraph should be star 6‐edge‐colorable. In this article, we first prove that it is NP‐complete to determine whether for an arbitrary graph G. This answers a question of Mohar. We then establish some structure results on subcubic multigraphs G with such that but for any , where . We finally apply the structure results, along with a simple discharging method, to prove that every subcubic multigraph G is star 6‐edge‐colorable if , and star 5‐edge‐colorable if , respectively, where is the maximum average degree of a multigraph G. This partially confirms the conjecture of Dvořák, Mohar, and Šámal.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".