From parametric warping to the cooperation of local features and global models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it