Quantitative laser-based x-ray fluorescence and particle-induced x-ray emission
Bibliographic record
Abstract
Abstract In this work, we demonstrate the feasibility of quantitative laser-based x-ray fluorescence (XRF) combined with particle-induced x-ray emission (PIXE) (called XPIF for x-ray and particle-induced fluorescence) spectroscopy analysis for elemental composition in solid samples. A multi-hundred TW laser system accelerated protons and produced x-rays that were impinging on solid samples, inducing characteristic line emissions of the elements contained in the material. The x-ray yield obtained from the characteristic emissions for each element can be related to its mass concentration using both the thick PIXE and thick XRF formalism. This is performed by using of an iterative numerical procedure. We tested the validity of our method on three homogeneous metallic materials, stainless steel, bronze and brass. The mass proportions of these samples retrieved by our analysis (XPIF) is within the errors bars compared with a commercial energy dispersive x-ray spectrometer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".