Multiprobe estimation of field line curvature radius in the equatorial magnetosphere and the use of proton precipitations in magnetosphere‐ionosphere mapping
Bibliographic record
Abstract
Nonadiabatic, chaotic motion of ions under a curved geomagnetic field geometry is well recognized as a key mechanism that leads to the pitch angle scattering of protons in the equatorial magnetosphere. The efficiency of such proton pitch angle scattering process is energy dependent and controlled by the radius of curvature of the magnetic field line (RCMFL). The protons scattered into the loss cone will precipitate into the ionosphere and excite proton auroras there. In this study, we propose techniques to calculate the RCMFL based upon multiprobe measurements under different probe geometries. We also demonstrate how to use the obtained RCMFL to evaluate the proton precipitation fluxes, and further use the proton precipitation features to help estimate the magnetosphere‐ionosphere mapping. Our procedures are summarized as follows: (1) We calculate the RCMFL using Time History of Events and Macroscale Interactions during Substorms (THEMIS) multiprobe measurements. (2) With the obtained RCMFL, we evaluate the scattering efficiency and in turn the loss cone fluxes of the magnetospheric protons at different energy levels. (3) The above evaluation of the in situ loss cone fluxes is compared to the ionospheric measurements of proton precipitation fluxes in various energy ranges and/or the optical proton auroras to estimate the possible footprint of THEMIS probes in the ionosphere.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".