Bibliographic record
Abstract
Medial Representations of Objects A medial representation of an object describes a locus midway between (at the center of a sphere bitangent to) two sections of the boundary, and gives the distance to the boundary, called the medial radius. The object is obtained as the union of overlapping bitangent spheres. This results in a locus of ( p , r ), where p gives the sphere center and r gives the radius of the sphere. In some representations, the vectors from the medial point to the two or more corresponding boundary points are included; in others they are derived. The Blum medial axis is a transformation of an object boundary that has the same topology as the object; thus, the boundary can generate the medial locus ( p , r ), and the latter can also generate the object boundary. In the first direction the transformation is a function, but in the second direction it is one-to-many, because a medial point describes more than one boundary point. One of the strengths of using the medial representation as a primitive is that any unbranching, connected subset of the medial locus generates intrinsic space coordinates for the part of the object interior corresponding to it. These coordinates include positional location in the medial sheet, a choice of spoke (left or right) and length along that spoke.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".