Surface characterization of Asian lacquers using surface metrology and data science: introducing the roughness spectrum
Bibliographic record
Abstract
Abstract A quantitative approach to the study of Asian lacquer surfaces combining non-contact, non-invasive, and non-destructive surface metrology and data science techniques is presented. The lacquers within this artistic tradition–laccol, thitsi, and urushi– combined with various additives possess quantifiable differences in the surface texture/topography that may be used to detect and identify a lacquer type from non-perturbing/contact measurements. This study examined laccol, thitsi, and urushi lacquer handmade test panels with various oils, pigments, and resins before and after aging. Confocal microscopy was used to acquire quantitative surface texture areal data from the test panels and a set of works of art and cultural heritage objects. Data science methods of feature engineering and convolutional neural networks (CNN) were applied to analyze the numerical surface texture data, assign lacquer specimens to the three lacquer types, and quantify the surface changes associated with aging.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".