Bibliographic record
Abstract
A perfect dominating set in a graph G = (V, E) is a subset S ⊆ V such that each vertex in V \ S has exactly one neighbor in S . A perfect coalition in G consists of two disjoint sets of vertices V 1 and V 2 such that i) neither V 1 nor V 2 is a dominating set, ii) each vertex in V(G) \ V 1 has at most one neighbor in V 1 and each vertex in V(G) \ V 2 has at most one neighbor in V 2 , and iii) V 1 ∪ V 2 is a perfect dominating set. A perfect coalition partition (abbreviated prc -partition) in a graph G is a vertex partition π = {V 1 , V 2 , …, V k } such that for each set V i of π , either V i is a singleton dominating set or there exists a set V j ∈ π that forms a perfect coalition with V i . In this paper, we initiate the study of perfect coalition partitions in graphs. We obtain a bound on the number of perfect coalitions involving each member of a perfect coalition partition, in terms of maximum degree. The perfect coalition of some special graphs are investigated. Graphs with minimum degree one, triangle-free graphs and trees with large perfect coalition numbers are investigated.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".