Path homomorphisms, graph colorings, and boolean matrices
Bibliographic record
Abstract
Abstract We investigate bounds on the chromatic number of a graph G derived from the nonexistence of homomorphisms from some path \documentclass{article}\footskip=0pc\pagestyle{empty}\begin{document}\begin{eqnarray*}\vec{P}\end{eqnarray*}\end{document} into some orientation \documentclass{article}\footskip=0pc\pagestyle{empty}\begin{document}\begin{eqnarray*}\vec{G}\end{eqnarray*}\end{document} of G . The condition is often efficiently verifiable using boolean matrix multiplications. However, the bound associated to a path \documentclass{article}\footskip=0pc\pagestyle{empty}\begin{document}\begin{eqnarray*}\vec{P}\end{eqnarray*}\end{document} depends on the relation between the “algebraic length” and “derived algebraic length” of \documentclass{article}\footskip=0pc\pagestyle{empty}\begin{document}\begin{eqnarray*}\vec{P}\end{eqnarray*}\end{document} . This suggests that paths yielding efficient bounds may be exponentially large with respect to G , and the corresponding heuristic may not be constructive. © 2009 Wiley Periodicals, Inc. J Graph Theory 63: 198–209, 2010
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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 | 0.003 | 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".