High‐energy PIXE using very energetic protons: quantitative analysis and cross‐sections
Bibliographic record
Abstract
Abstract Latterly, PIXE using high‐energy protons has been applied effectively for the qualitative analysis of archaeological and art objects, providing information from deep inside the object. This is due to the high cross‐sections for the excitation of K‐lines of heavy elements together with the large penetration depth of high‐energy protons, resulting in analysable depth of up to several millimetres. After the extension of the GUPIX software package to proton energies of up to 100 MeV, quantitative analysis came within reach. Measurements on thin and thick metal targets, and also on alloy standards with known composition and various thickness, were performed. The concentrations obtained were compared with the certified values. The agreement was good for samples with a thickness of around 2 mm. However, for several centimetre thick samples, the heavy elements were overestimated when using the K‐lines of these elements for the data evaluation. To clarify this, K‐shell cross‐section measurements were carried out for various Z . The measurements and the results are presented and discussed. Copyright © 2005 John Wiley & Sons, Ltd.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".