Bibliographic record
Abstract
Edges in an image arise from discontinuities in scene variables, namely reflectance (R), illumination (I), depth (D) and surface orientation (O). Prior studies on edge classification have viewed it as a binary classification problem: each edge is assumed to arise from one of two disjoint categories (e.g., depth or not depth, shadow or not shadow). Here we suggest an alternate view in which an edge may signal discontinuities in any combination of the scene variables (RIDO). To explore this model, we had 4 trained observers label one randomly selected edge in each of 1,000 randomly selected images drawn from the McGill Calibrated Colour Image Database and from Flickr; the averaged data provide an estimate of the joint distribution over the 4 binary variables RIDO. To model the distribution, we consider an exhaustive set of 42 undirected and 72 directed graphical models of low-order (parameters). The models first link the RI variables, capturing the constraint but also the fact that the two variables change together less often than would be predicted by independence – it’s usually one or the other. The IO variables are then linked, which captures the fact that illumination is more likely to be changing if surface orientation is changing. More subtle relations are most efficiently captured by directed models. For example, conditioning R on D captures the increased likelihood of reflectance change at occlusion boundaries.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".