Bibliographic record
Abstract
Central fuelling is a fundamental issue in the next generation tokamak-ITER (International Thermonuclear Experimental Reactor).It is essential for optimization of the bootstrap current which is proportional to the pressure gradient of trapped particles.The conventional fusion reactor fuelling techniques, such as gas puffing and cryogenic pellet injection, are considered inadequate to fulfill this goal due to premature ionization caused by high plasma temperature and density.Compact Torus (CT) injection is a promising fuelling technique for central fuelling a reactor-grade tokamak.An accelerated CT is expected to penetrate into the core region and deposit fuel there provided the CT kinetic energy density exceeds the magnetic energy density in a target plasma.This process is complicated and involves CT penetration into an external magnetic field, a CT stopping mechanism, magnetic reconnection, and excitation of plasma waves.CTs can be injected at different angles with respect to the tokamak toroidal magnetic field, either horizontally or vertically.Normally, CTs are injected radially His untiring willingness to teach and encourage me made the whole course of this thesis work highly enjoyable.His dedication to high quality experimental plasma physics research has been a great inspiration.I am especially fortunate to have witnessed Prof. Xiao's talent for identifying important research problems and solving technical problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".