Development and applications of 3D-DIVIMP(HC) Monte Carlo impurity modeling code
Bibliographic record
Abstract
A self-contained gas injection system for the Divertor Material Evaluation System (DiMES) on DIII-D, the Porous Plug Injector (PPI), has been employed by A. McLean for in-situ study of chemical erosion in the tokamak divertor environment by injection of CH4. The principal contribution of the present thesis is a new interpretive code, 3D-DIVIMP(HC), which has been developed and successfully applied to the interpretation of the CH, C I, and C II emissions measured during the PPI experiments. The two principal types of experimental data which are compared here with 3D-DIVIMP(HC) code modeling are (a) absolute emissivities measured with a high resolution spectrometer, and (b) 2D filtered camera (TV) pictures taken from a view essentially straight down on the PPI. Incorporating the Janev-Reiter database for the breakup reactions of methane molecules in a plasma, 3D-DIVIMP(HC) is able to replicate these measurements to within the combined experimental and database uncertainties. It is therefore concluded that the basic elements of the physics and chemistry controlling the breakup of methane entering an attached divertor plasma have been identified and are incorporated in 3D-DIVIMP(HC).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".