13C-Tracer Experiments in DIII-D Preliminary to Thermal Oxidation Experiments to Understand Tritium Recovery in DIII-D, JET, C-Mod, and MAST
Bibliographic record
Abstract
Retention of tritium in carbon co-deposits is a serious concern for ITER. Developing a reliable in-situ removal method of the co-deposited tritium would allow the use of carbon plasma-facing components which have proven reliable in high heat flux conditions and compatible with high performance plasmas. Thermal oxidation is a potential solution, capable of reaching even hidden locations. It is necessary to establish the least severe conditions to achieve adequate tritium recovery, minimizing damage and reconditioning time. The first step in this multi-machine project is {sup 13}C-tracer experiments in DIII-D, JET, C-Mod and MAST. In DIII-D and JET, {sup 13}CH{sub 4} has been (and in C-Mod and MAST, will be) injected toroidally symmetrically, facilitating quantification and interpretation of the results. Tiles have been removed, analyzed for {sup 13}C content and will next be evaluated in a thermal oxidation test facility in Toronto with regard to the ability of different severities of oxidation exposure to remove the different types of (known and measured) {sup 13}C co-deposit. Removal of D/T from B on Mo tiles from C-Mod will also be tested. OEDGE interpretive code analysis of the {sup 13}C deposition patterns is used to generate the understanding needed to apply findings to ITER. First results are reported here for the {sup 13}C injection experiments IN DIII-D.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".