Study of Wall Recycling and Conditioning on the Hanbit Mirror Device
Bibliographic record
Abstract
The wall recycling effect dominantly appears in the ICRH discharge with ω < ωci in the HANBIT plasma. The methods and evaluation of wall conditioning are described. The progress of wall conditioning is monitored with neutral pressure and plasma parameters. Electron cyclotron resonance–discharge cleaning(ECR-DC) is applied to improve wall conditioning, and then electron impact desorption(EID) by filament heating is utilized in order to desorb the impurities from the wall. The impurities are analyzed quantitatively by quadrupole mass spectrometer(QMA). We also install new baking system by Halogen lamp radiation with 2 kW in the HANBIT central cell. It is also observed that Hα emission reduces after lamp heating. The evolution of neutral pressure profiles are carefully evaluated during discharge and monitored discharge cleaning effect after several hundred of radio frequency(rf) shots. The partial pressure of light impurities much reduced after rf discharges The line integrated density and edge density much decreased after rf shots, while edge temperature increases. After ECR-DC, also line density decreases, but edge temperature much increases. Plasma beta goes up more than three times after 250 rf shots.
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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 | 0.001 |
| 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".