Bibliographic record
Abstract
The recognition of object categories has a rich history in computer vision. In the 1970s, generic object recognition systems sought to model and recognize objects based on their coarse, prototypical shape. These early systems employed complex 3-D models, which offered invariance to viewpoint (including image translation, rotation, and scale), articulation, occlusion, and minor within-class shape deformation. Despite powerful modeling paradigms, however, these early systems lacked the low- and intermediate-level segmentation, grouping, and abstraction machinery needed to recover prototypical shapes from real images of real objects. Over the next two decades, the recognition community began to back away from this “holy grail” of recognition, bringing new models closer to the image in an effort to reduce the representational gap between extractable image features and model features. During this time, the community migrated from the CAD-based vision era, in which exact 3-D geometry was specified, to the appearance-based vision era, in which exact 2-D photometry was specified (either globally, or locally at interest points). Almost in parallel, approaches to biological vision have followed a roughly similar path; that is, there has been a migration from CAD-inspired structural models comprised of 3-D parts, to image-based models preserving much of an object's input appearance, to, most recently, hybrid fragment-based models that rely on hierarchies of more localized image features. Over this period, the recognition problem was sometimes reformulated from generic object recognition to exemplar recognition. For the first time, real object exemplars, with full texture and complex shape, could be recognized.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.406 | 0.282 |
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".