Decision Process for Comparison of Partial and Complete XANES Spectra
Bibliographic record
Abstract
If the range of XANES spectra varies between sets of scans, it may be impossible to compare sets of spectra unless a restricted part of the spectra is used. The paper derives a decision process for comparison of partial and complete XANES spectra, taking lead as an example. Lead L3‐edge XANES spectra were collected at the Australian National Beamline Facility (BL‐20B) Photon Factory, Tsukuba, Japan over the energy range 13,000–13,150 eV (ring conditions: 2.5 GeV, 300–400 mA). The monochromator step size was reduced to 0.25 eV per step in the XANES region (13,000–13,100 eV and 13,040–13,100 eV) to collect high‐resolution spectra. XANES data for samples and model compounds were collected at ambient temperature and pressure in fluorescence, using simultaneous collection of a Pb metal reference foil for energy calibration (first derivative peak of elemental Pb was 13,050 eV). XANES spectra were fitted using spectral deconvolution and least‐squares linear combination fitting (LCF). Detailed XANES results with LCF approach over various energy ranges are illustrated and discussed and show the applicability of the decision‐making procedure to compare sets of spectra.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".