Periodic Feature Characterization in Nanostructured Surfaces and Emulsions
Bibliographic record
Abstract
Understanding structure–function relationships is essential to advance the manufacturing of next-generation materials with desired properties and functionalities. Precise and rapid measurement of features like wrinkle size, droplet diameter, and surface roughness is essential to establishing such structure–function relationships. To this end, this work developed feature size and surface morphology characterizations through image analysis in Python and validated them with both synthetic and experimental images. Manual measurements of biobased surfaces resulted in errors between 3.3% ( N = 50; visually simple) and 51.2% ( N = 100; visually complex) compared to the results of Python analysis. This analysis was also used to accurately distinguish multiple feature size populations in a given image (which were missed entirely in manual measurements) and to determine the skewness and kurtosis of biological surfaces in a surface roughness map. This work contributes to the larger goal of developing a robust and computationally cheap platform to analyze complex materials to accelerate structure–function discovery.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".