Bibliographic record
Abstract
For a family \(\mathcal F\) of graphs, a graph \(G\) is said to be \(\mathcal F\)-free if \(G\) contains no member of \(\mathcal F\) as an induced subgraph. We let \(\mathcal G_{3}(\mathcal F)\) be the family of \(3\)-connected \(\mathcal F\) -free graphs. Let \(P_{n}\) and \(C_{n}\) denote the path and the cycle of order \(n\), respectively. Let \(T_{0}\) be the tree of order nine obtained by joining a pendant edge to the central vertex of \(P_{7}\). Let \(T_{1}\) and \(T_{2}\) be the trees of order ten obtained from \(T_{0}\) by joining a new vertex to a vertex of \(P_{7}\) adjacent to an endvertex, and to a vertex of \(P_{7}\) adjacent to the central vertex, respectively. We show that \(\mathcal G_{3}(\{C_{3}, C_{4}, T_{1}\})\) and \(\mathcal G_{3}(\{C_{3}, C_{4}, T_{2}\})\) are finite families.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".