Temporal evolution of confined fast-ion velocity distributions measured by collective Thomson scattering in TEXTOR
Bibliographic record
Abstract
Fast ions created in the fusion processes will provide up to 70% of the heating in ITER. To optimize heating and current drive in magnetically confined plasmas insight into fast-ion dynamics is important. First measurements of such dynamics by collective Thomson scattering (CTS) were recently reported [Bindslev, Phys. Rev. Lett. 97, 205005 2006]. Here we extend the discussion of these results which were obtained at the TEXTOR tokamak. The fast ions are generated by neutral-beam injection and ion-cyclotron resonance heating. The CTS system uses 100-150kW of 110-GHz gyrotron probing radiation which scatters off the collective plasma fluctuations driven by the fast-ion motion. The technique measures the projected one-dimensional velocity distribution of confined fast ions in the scattering volume where the probe and receiver beams cross. By shifting the scattering volume a number of scattering locations and different resolved velocity components can be measured. The temporal resolution is 4ms while the spatial resolution is approximately 10cm depending on the scattering geometry. Fast-ion velocity distributions in a variety of scenarios are measured, including the evolution of the velocity distribution after turnoff of the ion heating. These results are in close agreement with numerical simulations.
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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".