Analyzing Tree Attachments in 2-Crossing-Critical Graphs with a V8 Minor
Bibliographic record
Abstract
The crossing number of a graph is the minimum number of pairwise edge crossings in a drawing of the graph in the plane. A graph G is k-crossing-critical if its crossing number is at least k and if every proper subgraph H of G has crossing number less than k. \n \nIt follows directly from Kuratowski's Theorem that the 1-crossing-critical graphs are precisely the subdivisions of K{3,3} and K5. Characterizing the 2-crossing-critical graphs is an interesting open problem. \n \nMuch progress has been made in characterizing the 2-crossing-critical graphs. The only remaining unexplained such graphs are those which are 3-connected, have a V8 minor but no V10 minor, and embed in the real projective plane. This thesis seeks to extend previous attempts at classifying this particular set of graphs by examining the graphs in this category where a tree structure is attached to a subdivision of V8. \n \nIn this paper, we analyze which of the 106 possible 3-stars can be attached to a subdivision H of V8 in a 3-connected 2-crossing-critical graph. This analysis leads to a strong result, where we demonstrate that if a k-star is attached to a V8 in a 2-crossing-critical graph, then k <= 4. Finally, we significantly restrict the remaining trees which still need to be investigated under the same conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".