Shapelet-based orientation and defect identification method for nanostructured surface imaging
Bibliographic record
Abstract
Abstract Structure-property relations are of fundamental importance for continued progress in materials research. Determining these relationships for nanomaterials introduces additional challenges, especially when nanostructure is present, either through self-assembly or nano-lithographic processes. Recent advances have been made for quantification of nanostructured surfaces, for which many robust experimental imaging methods exist. One promising approach is based on the use of shapelet functions for image analysis, which may be used as a reduced basis for surface pattern structure resulting from a broad range of phenomena (e.g. self-assembly). These shapelet-based methods enable automated quantification of nanostructured images, guided by the user/researcher, providing pixel-level information of local order without requiring detailed knowledge of order symmetries. In this work, enhancements to the existing shapelet-based response distance method are developed which enable further analysis of local order, including quantification of local orientation and identification of topological defects. The presented shapelet-based methods are applied to a representative set of images of self-assembled surfaces from experimental characterization techniques including scanning electron microscopy, atomic force microscopy, and transmission electron microscopy. These methods are shown to be complementary in implementation and, importantly, provide researchers with a robust and generalized computational approach to comprehensively quantify nanostructure order, including local orientation and boundaries within well-aligned grains.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".