Development of visible light tomographic imaging system for field-reversed configuration collisional merging experiment
Bibliographic record
Abstract
A visible light tomographic imaging system has been developed for the collisional merging experiment of field-reversed configurations (FRCs) on the FRC Amplification via Translation-Collisional Merging device at Nihon University. Two FRCs formed by field-reversed theta-pinch translate at super-Alfvénic velocity and collide with each other. The translation and collision processes are completed in 20-30 µs, and a single FRC is reformed in ∼70 µs. To study these translation and collisional merging processes, the tomographic system, including fast response tomographic cameras and a reconstruction method assuming a Rigid-Rotor (RR) model, is developed. The developed tomographic cameras simply consist of 16 channels of multi-anode photomultipliers, a band-pass filter, a slit, and a cylindrical lens, which expands the viewing angle. Because the viewing angle is limited by the size of the viewports of the metal chamber, the iterative method assuming the RR model has been applied to reconstruct tomographic images from a small number of projections. The developed tomographic imaging system can estimate the behavior of FRCs. Four cameras are installed in the two cross sections near the collision point. The radial shift of each translated FRC can be calculated by this system. Details of the developed tomographic camera system and RR reconstruction method are reported.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".