Runaway electron production in DIII-D killer pellet experiments, calculated with the CQL3D/KPRAD model
Bibliographic record
Abstract
Runaway electrons are calculated to be produced during the rapid plasma cooling resulting from “killer pellet” injection experiments, in general agreement with observations in the DIII-D [J. L. Luxon et al., Plasma Physics and Controlled Nuclear Fusion Research 1986 (International Atomic Energy Agency, Vienna, 1987), Vol. I, p. 159] tokamak. The time-dependent dynamics of the kinetic runaway distributions are obtained with the CQL3D [R. W. Harvey and M. G. McCoy, “The CQL3D Code,” in Proceedings of the IAEA Technical Committee Meeting on Numerical Modeling, Montreal, 1992 (International Atomic Energy Agency, Vienna, 1992), p. 489] collisional Fokker–Planck code, including the effect of small and large angle collisions and stochastic magnetic field transport losses. The background density, temperature, and Zeff are evolved according to the KPRAD [D. G. Whyte and T. E. Evans et al., in Proceedings of the 24th European Conference on Controlled Fusion and Plasma Physics, Berchtesgaden, Germany (European Physical Society, Petit-Lancy, 1997), Vol. 21A, p. 1137] deposition and radiation model of pellet–plasma interactions. Three distinct runway mechanisms are apparent: (1) prompt “hot-tail runaways” due to the residual hot electron tail remaining from the pre-cooling phase, (2) “knock-on” runaways produced by large-angle Coulomb collisions on existing high energy electrons, and (3) Dreicer “drizzle” runaway electrons due to diffusion of electrons up to the critical velocity for electron runaway. For electron densities below ≈1×1015 cm−3, the hot-tail runaways dominate the early time evolution, and provide the seed population for late time knock-on runaway avalanche. For small enough stochastic magnetic field transport losses, the knock-on production of electrons balances the losses at late times. For losses due to radial magnetic field perturbations in excess of ≈0.1% of the background field, i.e., δBr/B⩾0.001, the losses prevent late-time electron runaway.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".