Precision, accuracy, and range of perceived achromatic transparency
Bibliographic record
Abstract
How accurately do human observers perceive the properties of an achromatic transparent filter with both reflective and transmissive components? To address this question, a novel six-luminance stimulus was employed, consisting of three transparent layer luminances set against three background luminances, which satisfied the conventional constraints of perceptual transparency. In one experiment, subjects adjusted one of the three layer luminances to complete the impression of a uniform transparent disk. It was found that the luminance-based formulation of Metelli's episcotister model and a model based on ratios of Michelson contrasts best predicted the subjects' settings, which were both accurate and precise. In another experiment, pairs of stimuli selected from a range with various values of the adjustable layer luminance were presented in a series of forced-choice trials. A modified implementation of the pair comparisons method was employed to recover the distribution that describes each subject's preference pattern. Results showed that there exists a reasonably wide range of stimuli that give rise to at least some degree of perceived transparency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".