Bibliographic record
Abstract
An Italian dominating function on a graph $G$ is a function $f:V(G)\rightarrow \{0,1,2\}$ such that $\sum_{u\in N(v)}f(u)\geq 2$ for every vertex $v\in V_0$, where $V_0=\{v\in V(G) : f(v)=0\}$ and $N(v)$ represents the open neighbourhood of $v$. A starred Italian dominating function on $G$ is an Italian dominating function $f$ such that $V_0$ is not a dominating set of $G$. The starred Italian domination number of $G$, denoted $\gamma_{I}^*(G)$, is the minimum weight $\omega(f)=\sum_{v\in V(G)}f(v)$ among all starred Italian dominating functions $f$ on $G$. In this article, we initiate the study of the starred Italian domination in graphs. For instance, we give some relationships that exist between this parameter and other domination invariants in graphs. Also, we present tight bounds and characterize the extreme cases. In addition, we obtain exact formulas for some particular families of graphs. Finally, we show that the problem of computing the starred Italian domination number of a graph is NP-hard.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".