Significance of surface and bulk light scattering in microgloss and microgloss nonuniformity of coated papers: Influence of pigment characteristics and calendering conditions
Bibliographic record
Abstract
Abstract A custom-made imaging reflectometer was used to study the microgloss properties of coated papers. To quantify the relative contributions of surface and bulk scattering to the total intensity of reflected light, a pair of polarizer lenses was employed. The mean value and the standard deviation of local grey level of captured images at micro-scale (with a spatial resolution of 16 μm x 16 μm) were determined to quantify the average microgloss and microgloss nonuniformity of samples, respectively. It was found that surface microgloss; i.e. local light reflection in the specular direction due to surface light scattering, increased linearly with increasing the total microgloss of paper. However, bulk microgloss; i.e. local light reflection in the specular direction due to the bulk scattering, remained nearly constant for a wide range of coated papers with varying coating weight, formulation and calendering condition. The relative contribution of bulk scattering to the total microgloss increased as the sheet progressively became less glossy. On the other hand, microgloss nonuniformity was governed by the local variation in surface light scattering and was affected by pigment particle size, pigment morphology and calendering conditions. Finally, both surface microgloss and surface microgloss nonuniformity were power-law functions of the surface texture parameter (Lc/λ)/(σh/λ)2where Lc, σhand λ are correlation length, RMS surface roughness, and wavelength of incident light, respectively.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".