A METHOD TO OBTAIN LOWER BOUNDS FOR CIRCULAR CHROMATIC NUMBER
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Bibliographic record
Abstract
The circular chromatic number $\chi_c(G)$ of a graph $G$ is a very natural generalization of the concept of chromatic number $\chi(G)$, and has been studied extensively in the past decade. In this paper we present a new method for bounding the circular chromatic number from below. Let $\omega$ be an acyclic orientation of a graph $G$. A sequence of acyclic orientations $\omega_1$, $\omega_2$, $\omega_3,\ldots$ is obtained from $\omega$ in such a way that $\omega_1=\omega$, and $\omega_i$ ($i\geq 2$) is obtained from $\omega_{i-1}$ by reversing the orientations of the edges incident to the sinks of $w_{i-1}$. This sequence is completely determined by $\omega$, and it can be proved that there are positive integers $p$ and $M$ such that $\omega_i=\omega_{i+p}$ for every integer $i\geq M$. The value $p$ at its minimum is denoted by $p_\omega$. To bound $\chi_c(G)$ from below, the methodology we develop in this paper is based on the acyclic orientations $\omega_M, \omega_{M+1},\cdots,\omega_{M+p_\omega-1}$ of $G$. Our method demonstrates for the first time the possibility of extracting some information about $\chi_c(G)$ from the period $\omega_M, \omega_{M+1},\cdots,\omega_{M+p_\omega-1}$ to derive lower bounds for $\chi_c(G)$.
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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 | 0.002 | 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 | 0.001 | 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 it