Visible-Light Imaging of the Super-Alfvénic/Sonic Collisional Merging Process of Field-Reversed Configurations with a Contrast Medium-Mixed Plasmoid
Bibliographic record
Abstract
The collisional merging formation process of field-reversed configurations (FRCs) was observed by using a recently developed spectroscopy system. The shape and boundary of the two plasmoids during the merging process were visualized by the “contrast medium” mixing. One of the two initial plasmoids captured a small amount of contrast medium (e.g., helium) diffused in a part of the vacuum vessel during the translation process. The contrast medium-mixed plasmoid merged with the other plasmoid which is without a contrast medium. The contrast-enhanced shape and internal structure of two plasmoids were observed using the spectroscopic systems with the line spectrum of the contrast medium ion. The emission distribution of the contrast medium ions inside the plasmoid was verified by visible-light computed tomography, which revealed that the contrast medium ions were captured only inside one plasmoid. In addition, the emission distribution resembled the density profile in the rigid rotor model, which is a pressure equilibrium model of FRCs. After the collision, the contrast medium ions diffused into the other side. This suggests the beginning of the merging with the plasmoid on the other side. The optically evaluated timescale of the merging was comparable with that observed by the internal magnetic measurements.
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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 | 0.000 | 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) | 0.001 | 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".