Bibliographic record
Abstract
Abstract This chapter presents the elastic recoil detection (ERD) technique, an excellent complement to Rutherford backscattering spectrometry (RBS) and particle‐induced x‐ray emission (PIXE) for H detection on 0.5–2 MV accelerators. ERD with an absorber foil is easy to implement, featuring excellent sensitivity to light elements (H, He, Li) but with moderate depth resolution. This can be markedly improved by means of an electrostatic filter in place of the absorber. ERD using a heavy ion (HI) beam offers the possibility of acquiring a distinct spectrum for each element since the elements making the target are detected. HI‐ERD is therefore often more sensitive than RBS for which the signals of the different elements are superimposed on each other. The higher stopping power of HI gives access to improved depth resolution. If, historically, HI‐ERD required multi‐MV accelerators, recent implementations have been developed on 1.7 MV machines, the size used for RBS. However, more sophisticated detection systems are required to achieve such mass‐resolved, higher relative energy‐resolution detection. The larger uncertainty regarding the HI stopping power value also introduces higher uncertainty on the depth scale. Beam‐induced depth‐profile modification must always be monitored and can become significant with HI.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".