Bibliographic record
Abstract
A graph $G$ is induced matching extendable (shortly, IM-extendable) if every induced matching of $G$ is included in a perfect matching of $G$. The IM-extendable graph was first introduced by Yuan. A graph $G$ is nearly IM-extendable if $G \vee K_1$ is IM-extendable. We show in this paper that: (1) Let $G$ be a graph with $2n-1$ vertices, where $n \geq 2$. If for each pair of nonadjacent vertices $u$ and $v$ in $G$, $d(u)+d(v) \geq 2 \lceil {{4n}/{3}}\rceil-3$, then $G$ is nearly IM-extendable. (2) Let $G$ be a claw-free graph with $2n-1$ vertices, where $n \geq 2$. If for each pair of nonadjacent vertices $u$ and $v$ in $G$, $d(u)+d(v) \geq 2n-1$, then $G$ is nearly IM-extendable. Minimum degree conditions of nearly IM-extendable graphs and nearly IM-extendable claw-free graphs are also obtained in this paper. It is also shown that all these results are best possible.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".