The roles of power loss and momentum-pressure loss in causing particle-detachment in tokamak divertors: II. 2 Point Model analysis that includes recycle power-loss explicitly
Bibliographic record
Abstract
Abstract In the Part I companion paper, using a simple heuristic model it was demonstrated that, generically, volumetric loss of both pressure-momentum and power are required for particle detachment to occur. The volumetric power-loss fraction, f pwr − loss , was treated as a freely specifiable quantity; however, part of f pwr − loss is due to the hydrogenic recycle process at the target and in the present paper an extended 2 Point Model—the 2 PM with Recycle —is used that takes this into account explicitly. It is again demonstrated, but now using more physically realistic modeling of both pressure-momentum and power loss, that it is the combination of these two volumetric losses that is the cause of particle-detachment. For practical use, a convenient spreadsheet version is provided for the 2 PM with Recycle . It allows the user to specify the magnitudes and time-variation during a discharge of the 3 primary drivers of plasma conditions in the divertor: q ∣ ∣ u t , f radiation impurity t , and n u t or p totu t , where t (s) is the time in the discharge; it outputs values of plasma quantities at the target, T et t , Γ ∣ ∣ t t
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".