Computational Visualization of Semi-transparent Metallic Thin Films with Roughness
Bibliographic record
Abstract
We model the visual appearance of thin, semi-transparent metallic films coated on arbitrary three-dimensional substrates, incorporating effects including nanoscale film roughness, microscale substrate roughness, and source of light. Film reflectance is modeled by combining electrodynamic simulations with a modified version of the Schlick approximation, which is adapted and validated to describe the color appearance of thin semi-transparent metallic films with nanoscale, subwavelength roughness. Diffuse scattering originating from microscale roughness of the substrate and partial reflectance is described by a microfacet model. Photorealistic rendered images generated by our approach are qualitatively compared to photographs of fabricated thin film samples under similar lighting conditions. We render images of semi-transparent metallic films as a function of film thickness, multilayer composition, substrate type, nanoscale film roughness, microscale substrate roughness, and environmental lighting, yielding physically plausible results consistent with previously reported observations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".