Assessment of Collateral Effects of Thermo-Oxidation on DIII-D In-Vessel Components in Preparation for In Situ Oxidation in DIII-D
Bibliographic record
Abstract
Carbon-based codeposits formed in carbon-containing fusion devices have the potential to dominate tritium retention in the torus. One of the tritium removal techniques currently being studied is thermo-oxidation, which is unique in its ability to remove tritium from codeposits without mechanical intervention in the torus and in its ability to remove tritium from codeposits in tile gaps and shaded areas. In preparation for an oxidation experiment planned to be performed in DIII-D, we have investigated the potential collateral effects of thermo-oxidation on DIII-D in-vessel components. Laboratory oxidation experiments were performed at 2 Torr ([approximately]270 Pa) and 15 Torr ([approximately]2 kPa) O2 pressure and temperatures in the range 100 to 350°C (373 to 623 K) for 2 to 8 h. After oxidation, components were examined for visual or mechanical change, and when appropriate, mass changes were also obtained. In some cases, optical diagnostics were also performed. The specimens were mostly spare/surplus components and spanned a wide variety of materials and functions, e.g., cryopump components; structural, mechanical, and diagnostic components; and fast-wave antennas. The effect of oxidation was found to be negligible for nearly all DIII-D components and materials tested.
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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 | 0.001 |
| 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 | 0.000 |
| 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".