Systematic and collaborative approach to problem solving using X-ray photoelectron spectroscopy
Bibliographic record
Abstract
The methodology presented within this work is a result of years of interactions between many junior and senior X-ray Photoelectron Spectroscopy (XPS) users operating within the CasaXPS spectral processing and interpretation program framework. In particular, discussions arising from a series of workshops have been a significant source for developing the overall XPS data processing concept and are the motivation for creating this work. These workshops organized by the Institut des Matériaux Jean Rouxel (IMN), Nantes gather both experienced and novice users of XPS for a week of discourse in conceptual experiment design and the resulting data processing. However, the framework constructed and utilized within these workshops encouraged the dissemination of knowledge beyond XPS data analysis and emphasized the importance of a multi-disciplinary collaborative approach to surface analysis problem-solving. The material presented here embodies data treatment originating from data made available to the first CNRS Thematic Workshop presented at Roscoff 2013. The methodology described here has evolved over the subsequent workshops in 2016 and 2019 and currently represents the philosophy used in CasaXPS spectral data processing paradigm.
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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 | 0.029 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".