Bibliographic record
Abstract
Chromatic adaptation transforms generally rely on a variant of the von Kries transformation-method to account for changes in the LMS cone signals that occur when changing from one illuminant to another. Von Kries adaptation also often referred to as the coefficient rule method or the diagonal transformation method-adjusts the 3 color channels by independent scale factors. Since there generally are only 3 known quantities available, namely the ratio of the cone signals of the two adapting illuminants, a crucial aspect of the von Kries method is that it requires only 3 parameters to be specified. A 9-parameter, 3x3 matrix transformation would be more accurate, but it is generally not possible to determine the extra parameters. This paper presents a novel method of predicting the effect a change of illumination has on the cone signals, while still relying on only 3 parameters. To begin, we create a large set of 3x3 matrices representing illuminant changes based on a sizable database of typical illuminant spectra and surface spectral reflectances. Representing these 3x3 matrices as points in a 9-dimensional space, we then apply principal components analysis to find a 3-dimensional basis which best approximates the original matrix space. To model an illumination change, a 3x3 matrix is constructed using a weighted combination of the 3 basis matrices. The relative weights can be calculated based on the 3 standard cone ratios obtained from the illuminant pair. Tests show that the new method yields better results than von Kries adaptation with or without sensor sharpening.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".