Plasma Cleaning of Metallic Mirrors from Carbon-Containing Films – New Possibilities for In Situ Monitoring of the Efficiency of Wall Conditioning in Fusion Devices
Bibliographic record
Abstract
The method proposed for measuring the erosion rate of the carbon film, pre-deposited on the mirror-like surface of the test metallic samples, directly during wall conditioning procedures in a fusion device. The practical realization of the method provided at the DSM-2 stand where deuterium plasma produced in conditions of electron resonance at frequency 2.45 GHz used for cleaning the samples. For controlling C-film thickness the time variation of electrical conductivity of the circuit ‘film+plasma+entire scheme’ was measured. The final cleaning stage sets according to the saturation section corresponding to the resistance of the entire measuring scheme. To check the state of full purification of samples from a carbon-containing film the reflectance at normal incidence in the wavelength 220-650 nm was measured before C-film deposition, just after C-film deposition, and after finishing the cleaning procedure. In all cases (16 experiments) the approach of total resistance to the ‘entire resistance’ of the scheme in use was supported by restoration of the reflectance of stainless steel samples to its initial value. The method can be reversed, i.e. allows one to control in situ the appearance of a contaminating layer growing on the surface of a metal sample, preliminary cleaned before being installed in a vacuum vessel
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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".