Factorizations and characterizations of induced‐hereditary and compositive properties
Bibliographic record
Abstract
Abstract An Erratum has been published for this article in Journal of Graph Theory 50:261, 2005 . A graph property (i.e., a set of graphs) is hereditary (respectively, induced‐hereditary) if it is closed under taking subgraphs (resp., induced‐subgraphs), while the property is additive if it is closed under disjoint unions. If ${\cal P}$ and ${\cal Q}$ are properties, the product ${\cal P}\circ {\cal Q}$ consists of all graphs G for which there is a partition of the vertex set of G into (possibly empty) subsets A and B with G[A] $\in {\cal P}$ and G[B] $\in {\cal Q}$ . A property is reducible if it is the product of two other properties, and irreducible otherwise. We show that very few reducible induced‐hereditary properties have a unique factorization into irreducibles, and we describe them completely. On the other hand, we give a new and simpler proof that additive hereditary properties have a unique factorization into irreducible additive hereditary properties [J. Graph Theory 33 (2000), 44–53]. We also introduce analogs of additive induced‐hereditary properties, and characterize them in the style of Scheinerman [Discrete Math. 55 (1985), 185–193]. © 2005 Wiley Periodicals, Inc. J Graph Theory 49: 11–27, 2005
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".