Pedestal confinement and stability in JET-ILW ELMy H-modes
Bibliographic record
Abstract
Abstract New experiments in 2013–2014 have investigated the physics responsible for the decrease in H-mode pedestal confinement observed in the initial phase of JET-ILW operation (2012 Experimental Campaigns). The effects of plasma triangularity, global beta and neutrals on pedestal confinement and stability have been investigated systematically. The stability of JET-ILW pedestals is analysed in the framework of the peeling–ballooning model and the model assumptions of the pedestal predictive code EPED. Low D neutrals content in the plasma, achieved either by low D2 gas injection rates or by divertor configurations with optimum pumping, and high beta are necessary conditions for good pedestal (and core) performance. In such conditions the pedestal stability is consistent with the peeling–ballooning paradigm. Moderate to high D2 gas rates, required for W control and stable H-mode operation with the ILW, lead to increased D neutrals content in the plasma and additional physics in the pedestal models may be required to explain the onset of the ELM instability. The changes in H-mode performance associated with the change in JET wall composition from C to Be/W point to D neutrals and low-Z impurities playing a role in pedestal stability, elements which are not currently included in pedestal models. These aspects need to be addressed in order to progress towards full predictive capability of the pedestal height.
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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 | 0.001 |
| 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".