A parametric method for correcting polluted plasma current signal and its application on Keda Torus eXperiment
Bibliographic record
Abstract
We have developed a parametric method for eliminating the background component of the plasma current, which is measured by a Rogowski coil and polluted by the toroidal magnetic field in the vacuum vessel of the Keda Torus eXperiment (KTX) reversed field pinch (RFP) device. The method considers the toroidal magnetic field windings, the KTX vacuum chamber, and the Rogowski coil as a linear time-invariant system; in this case, a constant frequency response function characterizes the system. Using this response function, the current component caused by pollution from the toroidal magnetic field can be predicted exactly for an arbitrary input current to the toroidal magnetic field windings. Compared with the traditional proportional compensation method, the proposed method has great flexibility and universality and it is potentially applicable to cases in which the toroidal field current signal changes over time with plasma feedback signals. Furthermore, the method can be applied to other similarly affected signals, such as magnetic field signals. As an example, we have corrected the poloidal and toroidal magnetic field signals better to reveal the true physical processes for the RFP state.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".