2-Distance and 3-Distance Domination Numbers of the Sierpinski Star Graph
Bibliographic record
Abstract
The domination set D(G) in graph G=(V(G),E(G)) is a subset of the vertex set in graph G such that every vertex in V(G)\D(G) is adjacent to at least one vertex in D(G). The minimum cardinality of a domination set in graph G is called the domination number and is denoted as γ(G). The set S_k (G) is called the k-distance domination set in graph G if every vertex v in V(G)\S_k (G) has a distance of less than or equal to k from at least one vertex in S_k (G). The minimum cardinality of a k-distance domination set in graph G is called the k-distance domination number and is denoted as γ_k (G). This paper investigated the 2-distance and 3-distance domination sets in the Sierpinski Star graph SS_n and derived the number of 2-distance domination of γ_2 (SS_n)=1 for n<3 and γ_2 (SS_n)=3.3^(n-3) for n≥3, as well as the 3-distance domination number of γ_3 (SS_n)=1 for n<3 and γ_3 (SS_n)=3^(n-3) for n≥3.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".