From parametric warping to the cooperation of local features and global models
Bibliographic record
Abstract
This paper addresses the question of how to integrate local and global information-the goal being a stable mechanism to partition parametric data into meaningful classes without injecting a priori information about the data. To do this we introduce a novel framework to represent both local and global information and their interactions. Where both types of information are represented together in parameter space and together define a self-organisation or warping of the data. An unsupervised clustering analysis is then performed to extract from the parametric data classes that are stable and meaningful. As an example of this paradigm we consider the problem of shape decomposition. Here we describe how image discontinuities (i.e. curves, edges or local curvature) can be integrated with global parametric models that represent the image. The resulting class clusters are then equivalent to the inferred part decomposition. An example of how this process can be used is demonstrated by applying it to the specific problem of determining the parts of 3-D objects. Results on real laser rangefinder images of complex objects are presented.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".