An Asymmetrical Diffusion Framework for Junction Analysis
Bibliographic record
Abstract
The diffusion framework exhibits many promising properties for the pur-poses of junction analysis. In its most common form, images are diffused either isotropically or with respect to gradient information in an anisotropical fashion. This information is then collected into an orientational distribution function (ODF) and the resulting features are modeled as ’X’, ’Y ’ or ’T’-shaped junctions. Specific to the spatio-temporal domain, and in particular, a 2D spatio-temporal slice, points of kinetic-based occlusion are identified by T-junctions while points of kinetic-transparency form X-junctions. The challenge is that most forms of diffusion are symmetric in their representa-tion and are unable to properly distinguish between these two junction types. This work proposes to diffuse information asymmetrically and investigates the differences between weighting the iterative diffusion isotropically versus as an ODF-shaped region of influence function. 1
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".