An overview of EBIT data needed for experiments on laser-producedplasmas
Bibliographic record
Abstract
Data produced by an electron beam ion trap (EBIT) can be crucial for interpreting spectra from plasmas created by long-pulse lasers. Four example spectra are considered: (i) the spectra from the hot gold plasma in the laser deposition region of a hot hohlraum, (ii) the He-β spectra from an argon-doped imploding core of an inertial confinement fusion capsule, (iii) the polarization in spectral lines produced by hot electrons generated by laser-plasma parametric instabilities, and (iv) the spectra of the underdense plasma from an aerogel X-ray source. The EBIT data needed for these cases are: (i) the line positions for 3 → 2 transitions in open M shell gold ions, (ii) the Li-like satellite lines for the K-β transition in open L shell argon ions, (iii) the polarization of suitable X-ray lines at 30 keV, and (iv) the precise wavelengths of K lines of highly charged Si ions and the precise wavelengths of L and K lines of highly charged Ge ions.PACS Nos.: 52.25.Os, 52.38.–4, 52.38.Bv, 52.57.–z, 52.57.Fg, 52.59.Px, 52.70.–m 52.70.La
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".