As HERFD-XANES Data Processing Code for Synthetic Mixtures, LCF and PCA
Bibliographic record
Abstract
Analysis of As HERFD-XANES spectra *Note: for the purpose of this code and its description, the word "standard" and "reference", in regard to a known material, are used interchangeably. None of the materials used in this work are certified standard reference materials. Step 1. The raw data must be normalized and exported to a csv file. This can be done in free programs such as Athena (http://bruceravel.github.io/demeter/documents/Athena/index.html) or Larch (https://xraypy.github.io/xraylarch/). All standards should be included in this file. Step 2. The .csv file of the standards serves as an input for the code contained in Synthetic Standard Mixtures. This code creates a dataset of synthetic mixtures from all combinations of the standards, the dataset size, the number of standards to include in each mixture and the amount of random normally distributed noise to add to each mixture can be specified. Step 3. The code contained in LCF on Synthetic Mixtures conducts linear combination fitting on all of the synthetic mixtures (with noise) using the standards and then compares the original known mixing ratio to the ratio that was determined by the linear combination fitting. Step 4. The code contained in PCA takes a dataset of synthetic mixtures and performs PCA analysis to determine if a defined standard is a component of that dataset, it does this for the HERFD dataset and corresponding transmission data set. The main plot outputted compares fits to the standard with different numbers of components. The code also outputs a scree plot. Please refer to the written article for more information.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.119 | 0.064 |
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".