A critical review based on experiments of the self-consistent modelling leading to the power balance equation of surface waves produced plasmas
Bibliographic record
Abstract
Summary form only given, as follows. The authors have been conducting an extensive experimental study on one of the key parameters of high-frequency discharges, theta , which represents the average power dissipated to maintain an electron-ion pair. A self-consistent model (coupling the wave and plasma equations) can be used to predict the observed behavior of theta , especially in regard to whether the similarity law theta /p versus pa is obeyed. However, a closer examination of the experimental results shows discrepancies that could be connected with the frequency dependence of the electron energy distribution function (EEDF). In this model, another important factor that reflects the EEDF is the effective collision frequency for momentum transfer, nu . Knowledge of both theta and nu allows the discharge to be modeled completely, since the axial distribution of the electron density can then be predicted. An effective electric field intensity value can be estimated from the observed theta and nu , and the behavior of the EEDF with frequency can be qualitatively deduced.>
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".