Inference of plasma parameters from fixed-bias multi-needle Langmuir probes (m-NLP)
Bibliographic record
Abstract
Abstract New approaches are presented to infer plasma densities and satellite floating potentials from currents collected with fixed-bias multi-needle Langmuir probes (m-NLP). Using synthetic data obtained from kinetic simulations, comparisons are made with inference techniques developed in previous studies and, in each case, model skills are assessed by comparing their predictions with known values in the synthetic data set. The new approaches presented rely on a combination of an approximate analytic scaling law for the current collected as a function of bias voltage, and multivariate regression. Radial basis function regression (RBF) is also applied to Jacobsen et al ’s procedure (2010 Meas. Sci. Technol. 21 085902) to infer plasma density, and shown to improve its accuracy. The direct use of RBF to infer plasma density is found to provide the best accuracy, while a combination of analytic scaling laws with RBF is found to give the best predictions of a satellite floating potential. In addition, a proof-of-concept experimental study has been conducted using m-NLP data, collected from the Visions-2 sounding rocket mission, to infer electron densities through a direct application of RBF. It is shown that RBF is not only a viable option to infer electron densities, but has the potential to provide results that are more accurate than current methods, providing a path towards the further use of regression-based techniques to infer space plasma parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".