Factorizations and characterizations of induced‐hereditary and compositive properties
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it