On Multicolor Turán Numbers
Bibliographic record
Abstract
Abstract. We address a problem which is a generalization of Turán-type problems recently introduced by Imolay, Karl, Nagy, and Váli. Let [Formula: see text] be a fixed graph and let [Formula: see text] be the union of [Formula: see text] edge-disjoint copies of [Formula: see text], namely [Formula: see text], where each [Formula: see text] is isomorphic to a fixed graph [Formula: see text] and [Formula: see text] for all [Formula: see text]. We call a subgraph [Formula: see text] multicolored if [Formula: see text] and [Formula: see text] share at most one edge for all [Formula: see text]. Define [Formula: see text] to be the maximum value [Formula: see text] such that there exists [Formula: see text] on [Formula: see text] vertices without a multicolored copy of [Formula: see text]. We show that [Formula: see text] and that all extremal graphs are close to a blow-up of the 5-cycle. This bound is tight up to the linear error term.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".