Multiple-tracer TESPEL injection for studying impurity behaviour in a magnetically confined plasma
Bibliographic record
Abstract
Abstract A new diagnostic method with tracer-encapsulated solid pellet (TESPEL) injection with multiple tracers is developed to study impurity behaviour in a magnetically confined plasma. If a pellet contains multiple tracers, it becomes possible to compare the behaviour of different impurities simultaneously under the same plasma conditions. We injected a TESPEL into the Large Helical Device mainly with triple tracers: vanadium (V), manganese (Mn) and cobalt (Co). The Li-like lines in the vacuum ultraviolet range and the Kα lines in the soft x-ray range from these tracers are simultaneously observed with a time resolution of 50 ms. As the charges of the nuclei of intrinsic impurities, chromium (Cr) and iron (Fe), are in between those of the tracers, the behaviour of Cr and Fe can be studied quantitatively by knowing the number of tracer particles and also by comparing the emission intensity change due to the electron temperature change. It is observed that the tracer impurities remain in the plasma core region when the plasma density is higher than 5 × 1019 m−3. It is also observed that the intrinsic impurities cannot enter the core region when the plasma density is higher than the same level, although the two phenomena appear to be independent.
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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".