Voronoi tessellations in the CRT and continuum random maps of finite excess
Bibliographic record
Abstract
Given a large graph G and k agents on this graph, we consider the Voronoi tessellation induced by the graph distance. Each agent gets control of the portion of the graph that is closer to itself than to any other agent. We study the limit law of the vector Vor: = (V1/n, V2/n, …, Vk/n), whose i'th coordinate records the fraction of vertices of G controlled by the i'th agent, as n tends to infinity. We show that if G is a uniform random tree, and the agents are placed uniformly at random, the limit law of Vor is uniform on the (k – 1)-dimensional simplex. In particular, when k = 2, the two agents each get a uniform random fraction of the territory. In fact, we prove the result directly on the Brownian continuum random tree (CRT), and we also prove the same result for a “higher genus” analogue of the CRT that we call the continuum random unicellular map, indexed by a genus parameter g ≥ 0. As a key step of independent interest, we study the case when G is a random planar embedded graph with a finite number of faces. The main idea of the proof is to show that Vor has the same distribution as another partition of mass Int: = (I1/n, I2/n, …, Ik/n) where Ij is the contour length separating the i-th agent from the next one in clockwise order around the graph.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".