Disjoint dominating sets with a perfect matching
Bibliographic record
Abstract
In this paper, we consider dominating sets [Formula: see text] and [Formula: see text] such that [Formula: see text] and [Formula: see text] are disjoint and there exists a perfect matching between them. Let [Formula: see text] denote the cardinality of smallest such sets [Formula: see text] in [Formula: see text] (provided they exist, otherwise [Formula: see text]). This concept was introduced in [W. F. Klostermeyer, M. E. Messinger and A. Angeli Ayello, An eternal domination problem in grids, Theory Appl. Graphs 4(1) (2017) 23pp.] in the context of studying a certain graph protection problem. We characterize the trees [Formula: see text] for which [Formula: see text] equals a certain graph protection parameter and for which [Formula: see text], where [Formula: see text] is the independence number of [Formula: see text]. We also further study this parameter in graph products, e.g., by giving bounds for grid graphs, and in graphs of small independence number.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".