An Open-Source Engine for the Processing of Electron Backscatter Patterns: EBSD-Image
Bibliographic record
Abstract
An open source software package dedicated to processing stored electron backscatter patterns is presented. The package gives users full control over the type and order of operations that are performed on electron backscatter diffraction (EBSD) patterns as well as the results obtained. The current version of EBSD-Image (www.ebsd-image.org) offers a flexible and structured interface to calculate various quality metrics over large datasets. It includes unique features such as practical file formats for storing diffraction patterns and analysis results, stitching of mappings with automatic reorganization of their diffraction patterns, and routines for processing data on a distributed computer grid. Implementations of the algorithms used in the software are described and benchmarked using simulated diffraction patterns. Using those simulated EBSD patterns, the detection of Kikuchi bands in EBSD-Image was found to be comparable to commercially available EBSD systems. In addition, 24 quality metrics were evaluated based on the ability to assess the level of deformation in two samples (copper and iron) deformed using 220 grit SiC grinding paper. Fourteen metrics were able to properly measure the deformation gradient of the samples.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.034 |
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".