Characteristics of edge-localized modes in the experimental advanced superconducting tokamak (EAST)
Bibliographic record
Abstract
Edge-localized modes (ELMs) are the focus of tokamak edge physics studies because the large heat loads associated with ELMs have great impact on the divertor design of future reactor-grade tokamaks such as ITER. In the experimental advanced superconducting tokamak (EAST), the first ELMy high confinement modes (H-modes) were obtained with 1 MW lower hybrid wave power in conjunction with wall conditioning by lithium (Li) evaporation and real-time Li powder injection. The ELMs in EAST at this heating power are mostly type-III ELMs. They were observed close to the H-mode threshold power and produced small energy dumps (1–2% of the stored energy). Type-III ELMs produced a time-averaged peak heat flux of about 2 MW m −2 on the target plate, a value which is ∼10 times larger than that of ELM-free phases. A few isolated and large type-I-like ELM events were also observed in EAST with an energy loss of up to 5% of the stored energy. Statistically, the ELM frequencies are several hundred hertz and the frequency appears to decrease with q 95 , the safety factor at 95% of the flux surface. When an ion cyclotron resonance frequency wave was injected during the H-mode phases, the ELM repetition frequency increased immediately. The frequency and amplitude of type-III ELMs can be effectively influenced by puffing impurity argon gas.
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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 | 0.001 | 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".