The Hodge decomposition of shell current on the Keda Torus eXperiment device
Bibliographic record
Abstract
Abstract The Hodge decomposition is a valuable tool for uniquely decomposing total currents on the composite shell into three types: inductive current, halo current, and harmonic current, each with its specific physical meaning. During plasma disruptions, halo currents appear, essential for studying the wall’s thermal load and electromagnetic force. Furthermore, understanding halo currents is crucial for improving the existing methodologies by removing their effects on equilibrium reconstructions and instability analyses based on boundary magnetic probe data. On the Keda Torus eXperiment (KTX) device, radial and tangent halo currents can be simultaneously provided to locate the contact region during a minor disruption experimentally. Additionally, experimental results demonstrate that, in addition to the occurrence of halo current during minor disruption events, halo current is already present simultaneously with the generation of inductive current when a resistive wall mode exists. For devices that lack the capability to measure the two-dimensional shell current distribution on the entire shell, we propose a method to estimate inductive and halo currents only using a set of shell currents along the toroidal direction. This technique is demonstrated on the KTX device and provides an overall good approximation of the inductive and halo current distribution.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".