A GENERAL APPROXIMATION ALGORITHM FOR PLANAR MAPS WITH APPLICATIONS
Bibliographic record
Abstract
Given an unknown target planar map, we present an algorithm for constructing an approximation of the unknown target based on information gathered from linear probes of the target. Our algorithm is a general purpose reconstruction algorithm that can be applied in many settings. Our algorithm is particularly suited for the setting where computing the intersection of a line with an unknown target is much simpler than computing the unknown target itself. The algorithm maintains a triangulation from which the approximation of the unknown target can be extracted. We evaluate the quality of the approximation with respect to the target both in the topological sense and the metric sense. The correctness of the algorithm and the evaluation of its time complexity are also presented. Finally, we present some experimental results. For example, since generalized Voronoi diagrams are planar maps, our algorithm presents a simpler alternative method for constructing approximations of generalized Voronoi diagrams, which are notoriously difficult to compute.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".