Studying Advanced Materials at the REIXS Beamline
Bibliographic record
Abstract
Advanced materials, including: superconductors, light emitting materials and battery materials, play an ever increasing role in society today. Studying these materials is key to reducing overall energy consumption for everyday technology. Soft x-rays have the ideal energy for probing the electronic properties of typical elements in these materials; x-ray absorption spectroscopy (XAS) and x-ray emission spectroscopy (XES) are robust techniques to measure the electronic structure in general. More advanced techniques, resonant inelastic x-ray scattering (RIXS) and resonant soft x-ray scattering (RSXS) are invaluable for studying electron correlations in novel materials. In addition, resonant x-ray reflectometry (RXR) allows one to probe the electronic structure of interfaces in layered materials. The Resonant Elastic and Inelastic X-ray Scattering (REIXS) beamline at the Canadian Light Source (CLS) is a soft x-ray beamline specializing in photon-in/photon-out techniques including those mentioned above. There are many opportunities at REIXS for material scientists wanting to fully explore the electronic properties of their advanced materials. We will showcase the current capabilities offered at REIXS as well as discuss some future advancements.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.102 | 0.025 |
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".