On the Classification of Colored Textures From a Texture-Ranking Experiment: Observers Ability of Discrimination Quantification
Bibliographic record
Abstract
This paper is devoted to the construction of Fechnerian scales on a physical dimension of investigated colored textures. For this purpose, we considered the extension of the Mallows-Bradley-Terry model for the analysis of the data collected from a contrast-sorting experiment. A likelihood ratio test procedure was proposed in order to choose between the two following hypotheses: discrimination and non-discrimination between the investigated stimuli. In addition, post-hoc analyzes allowed us to find out which of the stimuli differ from the others. Our findings indicate that the subjective attribute of visual contrast appears to be a psycho physical scale that maps to the physical scale corresponding to the Michelson contrast. Mainly, the estimates of the model index of discrimination parameter of the stimuli show that the ability of the observers to discriminate between the textures according to the visual contrast varies with respect to the color ranges and the textures types. According to the luminance contrasts ability of discrimination, the Isotropic texture type is the best, followed by the Random-dots texture type, then by the Horizontal grating type and the Vertical grating type is the least. The Fechnerian scales on the physical dimension of the Michelson contrast of the colored textures depend on the chromaticness of the colored textures phases and the texture types. The psycho physical method of identification would be the best when determining the related thresholds.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".