Boundary plasma studies for a spherical tokamak with lithium walls
Bibliographic record
Abstract
Boundary plasma and plasma-material interactions are investigated for magnetised target fusion (MTF) applications. The General Fusion magnetised target fusion technology uses coaxial helicity injection (CHI) start-up which forms a spherical tokamak in a cavity with liquid lithium walls that will subsequently be compressed to fusion conditions Laberge, J. Fusion Energy (2019) The Plasma Injector 3 (PI3) experiment at General Fusion is a non-compressing experiment with solid lithium walls that studies the formation and quasi-steady state operation of a CHI spherical tokamak Carbajal et al. (2023). An explorative study is carried out for wall-limited versus diverted configurations for PI3 using the fluid edge transport code UEDGE. Experimental edge temperature and density profiles from triple Langmuir probes are used to establish realistic temperature and density profiles in UEDGE Rognlien et al. (1992) by adjusting the transport coefficients. In UEDGE, we model the wall-limited plasma via a thin limiter with various insertion depths. It is found that limiter depth and location are key parameters in determining radial profiles and sputtered lithium behaviour. Furthermore, it is found that the total sputtering of the limiter is significantly lower than the sputtering of the wall in some of the limiter configurations studied. Lithium ions and neutral behaviour are compared between limited and diverted configurations.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".