Scintillator based detector for fast-ion losses induced by magnetohydrodynamic instabilities in the ASDEX upgrade tokamak
Bibliographic record
Abstract
A scintillator based detector for fast-ion losses has been designed and installed on the ASDEX upgrade (AUG) tokamak [A. Herrmann and O. Gruber, Fusion Sci. Technol. 44, 569 (2003)]. The detector resolves in time the energy and pitch angle of fast-ion losses induced by magnetohydrodynamics (MHD) fluctuations. The use of a novel scintillator material with a very short decay time and high quantum efficiency allows to identify the MHD fluctuations responsible for the ion losses through Fourier analysis. A Faraday cup (secondary scintillator plate) has been embedded behind the scintillator plate for an absolute calibration of the detector. The detector is mounted on a manipulator to vary its radial position with respect to the plasma. A thermocouple on the inner side of the graphite protection enables the safety search for the most adequate radial position. To align the scintillator light pattern with the light detectors a system composed by a lens and a vacuum-compatible halogen lamp has been allocated within the detector head. In this paper, the design of the scintillator probe, as well as the new technique used to analyze the data through spectrograms will be described. A last section is devoted to discuss the diagnosis prospects of this method for ITER [M. Shimada et al., Nucl. Fusion 47, S1 (2007)].
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".