Bibliographic record
Abstract
A Steiner star for a set P of n points in Rd connects an arbitrary center point to all points of P, while a star connects a point p ∈ P to the remaining n−1 points of P. All connections are realized by straight line segments. Fekete and Meijer showed that the minimum star is at most √ 2 times longer than the minimum Steiner star for any finite point configuration in Rd. The maximum ratio between them, over all finite point configurations in Rd, is called the star Steiner ratio in Rd. It is conjectured that this ratio is 4/π = 1.2732... in the plane and 4/3 = 1.3333... in three dimensions. Here we give upper bounds of 1.3631 in the plane, and 1.3833 in 3-space, thereby substantially improving recent upper bounds of 1.3999, and √ 2 − 10−4, respectively. Our results also imply improved bounds on the maximum ratios between the minimum star and the maximum matching in two and three dimensions. Our method exploits the connection with the classical problem of estimating the maximum sum of pairwise distances among n points on the unit sphere, first studied by László Fejes Tóth. It is quite general and yields the first non-trivial estimates below √ 2 on the star Steiner ratios in arbitrary dimensions. We show, however, that the star Steiner ratio in Rd tends to √ 2, the upper bound given by Fekete and Meijer, as d goes to infinity. Our estimates on the star Steiner ratios are therefore much closer to the conjectured
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".