A Study of Three Modern Asian Lacquers Using Surface Metrology and Data Science/Analytics
Bibliographic record
Abstract
Abstract This paper presents a quantitative approach to the study of surfaces using surface metrology and data science techniques. Asian lacquer types with various additives have a quantifiable impact on the topography of lacquered surfaces that may be used to detect lacquer type from non-contact measurements. To understand the unaged and aged characteristics, 15 different formulas of Asian lacquer were prepared using laccol, thitsi, and urushi with a range of oils, pigments, and resins were examined. The surfaces of the Asian lacquers test specimens were studied using confocal microscopy to acquire quantitative surface texture data, and data science methods of feature engineering and convolutional neural networks (CNN) were applied to analyze the numerical surface texture data, and assign lacquer specimens to the three lacquer types. Correct classification rates reached as high as 96%.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".