On the Spanning and Routing Ratios of the Yao-Four Graph
Bibliographic record
Abstract
The Yao graph is a geometric spanner that was independently introduced by Yao [SIAM J. Comput., 1982] and Flinchbaugh and Jones [SIAM J. Algebr. Discret. Appl., 1981]. We prove that for any two vertices of the undirected version of the Yao graph with four cones, there is a path between them with length at most 13 + 5/√2 ≈ 16.54 times the Euclidean distance between the vertices, improving the previous best bound of approximately 54.62. We also present an online routing algorithm for the directed Yao graph with four cones that constructs a path between any two vertices with length at most 17 + 9/√2 ≈ 23.36 times the Euclidean distance between the vertices. This is the first routing algorithm for a directed Yao graph with fewer than six cones. The algorithm uses knowledge of the coordinates of the current vertex, the (up to) four neighbours of the current vertex, and the destination vertex to make a routing decision. It also uses one additional bit of memory. We show how to dispense with this single bit at the cost of increasing the length of the path to √{331 + 154√2} ≈ 23.43 times the Euclidean distance between the vertices.
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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 | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".