Bibliographic record
Abstract
In many natural scenes, shadows and shading, which are primarily luminance-defined features, proliferate. Hence one might expect that the chromatic variations of natural scenes, which more faithfully represent the layout of object surfaces, will contain relatively fewer and larger uniform regions than the luminance variations, i.e., will be more "patchy." This idea was tested using images of natural scenes that were decomposed into chromatic and luminance layers modeled as the responses of the red-green, blue-yellow, and luminance channels of the human visual system. Patchiness was defined as the portion of pixels falling within a +/- threshold in the bandpass-filtered image, averaged across multiple filter scales. The red-green layers were found to be the most patchy, followed by the blue-yellow layers, with the luminance layers the least patchy. The correlation between image-layer patchiness and the slope of the Fourier amplitude spectrum was small and negative for all layers, the maximum value being for red-green (-0.48). We conclude that the chromatic layers of natural scenes contain more uniform areas than the luminance layers and that this is unpredicted by the slope of the Fourier amplitude spectrum.
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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 | 0.000 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".