Bibliographic record
Abstract
The medial surface of a 3D object is comprised of the locus of centers of its maximal inscribed spheres. Interest in this representation stems from a number of interesting properties: i) it is a thin set, i.e., it contains no interior points, ii) it is nomotopic to the original shape, iii) it is invariant under rigid transformations of the object and iv) given the radius of the maximal inscribed sphere associated which each skeletal point, the object can be reconstructed exactly. Hence, it provides a compact representation while preserving the object's genus and making certain useful properties explicit, such as its local width. These properties have led to its application in a variety of domains, including the analysis and quantification of the shape of volumetric structures in medical images. Despite its popularity, its numerical computation remains non-trivial. Most algorithms are not stable with respect to small perturbations of the boundary, and heuristic measures for simplification are often introduced. The study of the medial surface is the subject of this thesis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".