Bibliographic record
Abstract
For any two vertices u and v in a graph G (digraph D, respectively), a u-v geodesic is a shortest path between u and v (from u to v, respectively). Let I(u, v) (ID(u, v), respectively) denote the set of all vertices lying on a u-v geodesic. For a vertex subset S, let IG(S) (ID(S), respectively) denote the union of all IG(u, v) (ID(u, v), respectively) for u, v ∈ S. The geodetic number g(G) (g(D), respectively) of a graph G (digraph D, respectively) is the minimum cardinality of a set S with IG(S) = V (G) (ID(S) = V (D), respectively). The geodetic spectrum of a graph G, denote by S(G), is the set of geodetic numbers of all orientations of graph G. The lower geodetic number is g−(G) = minS(G). The upper geodetic number is g+(G) = maxS(G). The main purpose of this paper is to investigate lower and upper geodetic numbers of graphs. Our main results in this paper are: (i) For any connected graph G and any spanning tree T of G, g−(G) ≤ l(T ), where l(T ) is the number of leaves of T . (ii) The conjecture g+(G) ≥ g(G) is true for chordal graphs or graphs with no 3-cycle or graphs of 4-colorable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".