The [a,b]-domination and [a,b]-total Domination of Graphs
Bibliographic record
Abstract
A subset $S$ of the vertices of $G = (V, E)$ is an $[a, b]$-set if for every vertex $v$ not in $S$ we have the number of neighbors of $v$ in $S$ is between $a$ and $b$ for non-negative integers $a$ and $b$, that is, every vertex $v$ not in $S$ is adjacent to at least $a$ but not more than $b$ vertices in $S$. The minimum cardinality of an $[a, b]$-set of $G$ is called the $[a, b]$-domination number of $G$. The $[a, b]$-domination problem is to determine the $[a, b]$-domination number of a graph. In this paper, we show that the [2,b]-domination problem is NP-complete for $b$ at least $3$, and the [1,2]-total domination problem is NP-complete. We also determine the [1,2]-total domination and [1,2] domination numbers of toroidal grids with three rows and four rows.
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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 | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".