Exponentially many 4‐list‐colorings of triangle‐free graphs on surfaces
Bibliographic record
Abstract
Abstract Thomassen proved that every planar graph G on n vertices has at least distinct L ‐colorings if L is a 5‐list‐assignment for G and at least distinct L ‐colorings if L is a 3‐list‐assignment for G and G has girth at least five. Postle and Thomas proved that if G is a graph on n vertices embedded on a surface Σ of genus g , then there exist constants such that if G has an L ‐coloring, then G has at least distinct L ‐colorings if L is a 5‐list‐assignment for G or if L is a 3‐list‐assignment for G and G has girth at least five. More generally, they proved that there exist constants such that if G is a graph on n vertices embedded in a surface Σ of fixed genus g , H is a proper subgraph of G , and ϕ is an L ‐coloring of H that extends to an L ‐coloring of G , then ϕ extends to at least distinct L ‐colorings of G if L is a 5‐list‐assignment or if L is a 3‐list‐assignment and G has girth at least five. We prove the same result if G is triangle‐free and L is a 4‐list‐assignment of G , where , and .
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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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.012 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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".