Bibliographic record
Abstract
Abstract Particle‐Induced X‐Ray Emission (PIXE) is an analytical technique established in the decade of 1970s, which makes use of MeV ion beams (mainly proton beams) to induce x‐ray emission from materials. It is used both in macro mode, where beam spots are of the order of millimeters, and in micro mode, where ion beams are focused to micrometer size (or even below this, since 100 nm spots are already available for PIXE). It has major advantages for the analysis of sensitive and/or complex samples because it is essentially a non‐destructive technique. For example, some laboratories are equipped with facilities where the beam is extracted into the atmosphere, allowing the analysis of paintings and other cultural heritage samples. Recent developments also make possible the analysis of samples that are not homogeneous but composed of different layers. In this case, PIXE has large advantages relative to electron microprobe analysis (EPMA) because the particle beam scattering is very small, such that the micro‐beam remains focused along several micrometers into the sample. Finally, the use of x‐ray detection systems having high resolution (resolving power of the order of 100 or more) is presently moving from pure fundamental work towards applications as more complex samples need to be analyzed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.035 | 0.013 |
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".