Simulation Inference of Plasma Parameters From Langmuir Probe Measurements
Bibliographic record
Abstract
Abstract A novel approach is presented to infer plasma parameters from Langmuir probe measurements. Three‐dimensional kinetic simulations capable of accounting for multiple physical processes and realistic measurement conditions, are used to construct synthetic data sets, or solution libraries, consisting of computed probe collected currents, with corresponding plasma environment parameters. Applying the Swarm standard data analysis procedure to infer density and temperatures, and comparing them with values used in the simulations, provides an assessment of the uncertainties resulting from these procedures. A significant source of discrepancy is found to come from the neglect of minority light ions, and the effect of the sheath of the nearby satellite. An empirical relation is constructed, which reproduces the plasma density within 5%, given probe voltages and collected currents, and known temperatures, effective masses, plasma flow speeds, and the satellite floating potential. This relation is presented as a useful constraint between these several variables; not all of which being measured or estimated accurately in situ.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| 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".