Quantitative laser-based x-ray fluorescence and particle-induced x-ray emission
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 it