Inference of fixed bias probe measurements - A machine learning approach
Bibliographic record
Abstract
Abstract A new approach is presented to improve inferences made with Langmuir probe measurements in plasma while providing estimates of uncertainties. The technique makes use of a combination of computer simulations, analytic approximations, and multivariate regressions. It involves training inference models using currents collected by two probes, biased at different fixed potentials relative to a satellite. An advantage with fixed bias probes is their higher temporal and spatial resolution, compared to the more standard mode operation where bias voltages are swept in time. Synthetic data used to train models are constructed from kinetic simulations with assumed satellite and plasma parameters relevant to satellites in Low Earth Orbit (LEO). Simulations are made assuming a truncated Swarm geometry, the two Electric Field Instruments (EFI) Langmuir probes, and different satellite potentials in the range [-3, 0] V. Models are constructed using two approaches, with two-tuples of currents as independent variables. Inference skills are assessed with different metrics, from comparisons between inferred plasma and satellite parameters, and known values used as input in the simulations. The models are then applied to infer the same physical parameters, using Swarm A in situ measured currents, and comparing them with values reported on the Swarm data portal. Inferences are consistent with values reported on the Swarm data portal, although it is not possible at this point to determine if, and if so, to what extent, the simulation-regression approach leads to better inferences.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".