Experimental study of geometric <i>t</i> -spanners
Why this work is in the frame
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Bibliographic record
Abstract
The construction of t -spanners of a given point set has received a lot of attention, especially from a theoretical perspective. In this article, we experimentally study the performance and quality of the most common construction algorithms for points in the Euclidean plane. We implemented the most well-known t -spanner algorithms and tested them on a number of different point sets. The experiments are discussed and compared to the theoretical results, and in several cases, we suggest modifications that are implemented and evaluated. The measures of quality that we consider are the number of edges, the weight, the maximum degree, the spanner diameter, and the number of crossings. This is the first time an extensive comparison has been made between the running times of construction algorithms of t -spanners and the quality of the generated spanners.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 it