Modeling the Effects of Filament Radius and Photochemical Dye Fading on the Perceived Color Depth of a Filament Array
Bibliographic record
Abstract
An optical model is used for simulating the effects of varying filament radii on the perceived color strength of an array of parallel filament layers representing a textile fabric. As the filament radius decreases, the color yield also decreases in direct proportion. The model predicts that the concentration of dye in the filaments, required to achieve a given color strength, is inversely proportional to the filament radius in agreement with practical experience in dyeing microfiber fabrics. New equations are also derived to calculate the light absorption by each individual filament layer at a given dye concentration. The degree of photochemical dye fading in each layer is simulated using these respective absorbed light intensities leading to a decrease in the overall color strength. Repeated calculations of the absorbed light intensities and decreasing dye concentration and color strength show that pale colors will fade more rapidly than deep colors and that fading will be faster when the filament radius is decreased. Both these results agree with practical observations of the light fastness of dyeings. For deep colors, the upper filament layers absorb all the incident light, and filaments in the center of the array undergo minimal fading, resulting in significant dye concentration gradients in the filament matrix.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".