Segmented plasma flow meter response from kinetic simulations
Bibliographic record
Abstract
A relatively simple design of a segmented flow meter (SF meter) is presented for measuring in situ plasma flow velocities and other space plasma parameters. The response of the flow meter to space environment is simulated for plasma conditions representative of the ionosphere at mid and low latitudes using a Particle In Cell (PIC) code. A synthetic data set consisting of ion currents collected by several segments of the flow meter, and the physical parameters for which they were calculated, is then used to construct a solution library from which inference models can be constructed, using radial basis function (RBF) and neural network regressions. Simulation results show that with such a flow meter, it should be possible to infer plasma flow velocities in the direction perpendicular to the ram direction, with uncertainties of 45 m/s or less. Models can also be constructed to infer plasma densities, with a relative error of 23 %. This work is presented as a first assessment and proof of concept for an original design of a simple and robust flow meter.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".