Bibliographic record
Abstract
For a graph \(G\), an Italian dominating function (IDF) is a function \(f: V(G) \rightarrow \{0,1,2\}\) such that all vertices labeled with 0 must have at least two neighbors assigned the label 1 or at least one neighbor assigned the label 2. The weight of \(f\), denoted by \(w(f)\), is calculated by summing all the labels assigned by the function. Let \(f\) be an IDF on \(G\) with a minimum weight, denoted as \(\gamma_I(G)\). If \(S\) is the set of vertices where \(f(v) > 0\), then an Inverse Italian Dominating Function (IIDF) \(f'\) is defined as an IDF on \(G\) such that \(f'(v) = 0\) for all \(v \in S\). The notation \(\gamma_{iI}(G)\) represents the Inverse Italian Domination Number of the graph \(G\), which is the minimum weight among all IIDFs on \(G\). In this paper, we find \(\gamma_{iI}(G)\) of graphs and characterize the graphs for which \(\gamma_I(G) = 2\) and \(3\), as well as those with \(\gamma_{iI}(G) = 2\) and \(3\). Additionally, we provide a characterization of trees and graphs that achieve the largest possible \(\gamma_{iI}(G)\).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".