Identifying the location of the OMP separatrix in DIII-D using power accounting
Bibliographic record
Abstract
In order to identify reliable scalings for the scrape-off layer (SOL) power width it is necessary to know the location of the separatrix in divertor tokamaks as accurately as possible, specifically its location at the outside midplane (OMP) the standard reference location. Two methods are described which use power accounting to improve the accuracy of identifying the location of the OMP separatrix. The first uses the infrared-measured deposited power profile at the outer target as the primary input, the ' method'. The second uses the measured power input to the SOL, obtained by subtracting the power radiated from inside the separatrix from the total heating power, the ' method'. These two power accounting methods are illustrated with the examples of 21 H-mode DIII-D discharges. High spatial resolution Thomson scattering measured profiles of n e and T e for the main SOL near the OMP are also used as primary input to the analysis; only between-edge localized mode data are used here. The Thomson profiles are used to calculate the electron parallel conducted heat flux profiles which are then matched to the measured and by adjusting the location of the OMP separatrix relative to that of the Thomson data. For these attached discharges, it is found that the values of given by the two power accounting methods agree to within ∼1 mm of each other and also to within ∼1 mm of the values given by the 'standard DIII-D method' described by Porter et al (1998 Phys. Plasmas 5 1410 ). The shifted results in only modest changes to the values of n e and T e at the OMP separatrix relative to the 'standard' values, increasing by 8% and by 20%.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".