Dataset for: Improving parent-austenite twinned grain reconstruction using electron backscatter diffraction in low carbon austenite
Bibliographic record
Abstract
Improving parent-austenite twinned grain reconstruction using electron backscatter diffraction in low carbon austenite Ruth M. Birch1*, T. Ben Britton1, W. J. Poole1 1. Department of Materials Engineering, University of British Columbia, Frank Forward Building, 309-6350 Stores Road, Vancouver, BC, Canada V6T 1Z4 *corresponding author: ruth.birch@ubc.ca --- Abstract: Thermomechanical controlled processing (TMCP) is widely used to optimize the final properties of high strength low alloy (HSLA) steels, via microstructure engineering. The room temperature microstructures are influenced by the high temperature austenite phase, and the austenite microstructure is commonlycan be accessed by reconstruction using electron backscatter diffraction (EBSD) data of the final microstructure. A challenge for reconstruction of the PAG parent austenite grain (PAG) microstructure and subsequent austenite grain size measurement is the presence of austenite-phase annealing twins, and we address this challenge with a new ‘re-sort’ algorithm. Our algorithm has been validated using the retained austenite regions (which were recovered via advanced pattern matching of EBSD patterns). We demonstrate that the re-sort algorithm improves the PAG reconstruction significantly, especially for the grain boundary network and correlation with other methods of grain size assessment and development of TMCP steels. --- Dataset includes: Higher quality figures EBSD dataset with/without pattern matching: 1mm map Specimen 1 Site 1 Map Data 1-Subset 1.h5oina 1mm map Specimen 1 Site 1 Map Data 1-Subset 1-PatternMatching.h5oina Code bundle
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.022 |
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".