Spectral and raw quasi in-situ energy dispersive X-ray data captured via a TEM analysis of an ODS austenitic stainless steel sample under 1 MeV Kr2+ high temperature irradiation
Bibliographic record
Abstract
The data presented in this article is related to the research experiment, titled: ‘Quasi in-situ energy dispersive X-ray spectroscopy observation of matrix and solute interactions on Y-Ti-O oxide particles in an austenitic stainless steel under 1 MeV Kr2+ high temperature irradiation’ (Brooks et al., 2017) [1]. Quasi in-situ analysis during 1 MeV Kr2+ 520 °C irradiation allowed the same microstructural area to be observed using a transmission electron microscope (TEM), on an oxide dispersion strengthened (ODS) austenitic stainless steel sample. The data presented contains two sets of energy dispersive X-ray spectroscopy (EDX) data collected before and after irradiation to 1.5 displacements-per-atom (~1.25×10−3 dpa/s with 7.5×1014 ions cm−2). The vendor software used to process and output the data is the Bruker Esprit v1.9 suite. The data includes the spectral (counts vs. keV energy) of the quasi in-situ scanned region (512×512 pixels at 56k magnification), along with the EDX scanning parameters. The.raw files from the Bruker Esprit v1.9 output are additionally included along with the.rpl data information files. Furthermore included are the two quasi in-situ HAADF images for visual comparison of the regions before and after irradiation. This in-situ experiment is deemed ‘quasi’ due to the thin foil irradiation taking place at an external TEM facility. We present this data for critical and/or extended analysis from the scientific community, with applications applying to: experimental data correlation, confirmation of results, and as computer based modeling inputs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".