Combined model for the H-mode pedestal and ELMs
Bibliographic record
Abstract
A model is developed for use in integrated modelling codes to predict the height, width and shape of the H-mode pedestal as well as the frequency and width of edge localized modes (ELMs). The model for the H-mode pedestal in tokamak plasmas is based on flow shear reduction of anomalous transport, while the periodic ELM crashes are triggered by MHD instabilities. The formation of the pedestal and the L–H transition in this model are the direct result of flow shear suppression of transport. Suppression of the anomalous transport enhances the role of neoclassical transport in the pedestal region. The ratio of suppression of anomalous thermal transport in electron and ion channels controls the ratio of electron to ion temperature at the top of the pedestal. Two mechanisms for triggering ELMs are considered. ELMs are triggered by ballooning modes if the pressure gradient exceeds the ballooning limit or by peeling modes if the edge current density exceeds the peeling mode criterion. The models for the pedestal and ELMs are used in a predictive integrated modelling code to follow the time evolution of tokamak discharges from L-mode through the transition from L-mode to H-mode, with the formation of the H-mode pedestal, and, subsequently, the triggering of ELMs. The objective is to produce self-consistent predictions of the width, height and shape of the H-mode pedestal and the frequency of ELMs. The dependences of pedestal temperature, pedestal width and ELM frequency as a function of plasma heating power, magnetic field and density are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".