Bibliographic record
Abstract
Visual perception combines top-down processes arising from participants individual histories, such as expectations and goals, and bottom-up processes that arise from visual stimuli properties, such as luminance and contrast. The precise control of low-level visual stimuli properties is essential when investigating visual perception. Without it, for instance, investigations of bottom-up processes are virtually impossible and investigations of top-down processes risk major confounds when testing and formulating hypotheses. The SHINE (spectrum, histogram, and intensity normalization and equalization) toolbox for MATLAB [1] allows precise control of images' Fourier amplitude spectra, the normalizing and scaling of luminance and contrast, and exact histogram specification optimized for perceptual visual quality. Following Willenbockel and Cols (2010) advices, here we present an adaptation of the SHINE toolbox, named SHINE_color, which extends SHINE functionalities by allowing the parametrical manipulation of low-level properties of both static and animated colorful images.•Parametric manipulation of low-level properties of colorful images•Spectrum, histogram, and intensity normalization and equalization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".