An Open-Source Engine for the Processing of Electron Backscatter Patterns: EBSD-Image
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it