Empirical estimates and theoretical predictions of the shorting factor for the THEMIS double‐probe electric field instrument
Bibliographic record
Abstract
Abstract Double‐probe electric field measurements on board spacecraft present significant technical challenges, especially in the inner magnetosphere where the ambient plasma characteristics can vary dramatically and alter the behavior of the instrument. We explore the shorting factor for the Time History of Events and Macroscale Interactions during Substorms electric field instrument, which is a scale factor error on the measured electric field due to coupling between the sensing spheres and the long wire booms, using both an empirical technique and through simulations with varying levels of fidelity. The empirical data and simulations both show that there is effectively no shorting when the spacecraft is immersed in high‐density plasma deep within the plasmasphere and that shorting becomes more prominent as plasma density decreases and the Debye length increases outside the plasmasphere. However, there is a significant discrepancy between the data and theory for the shorting factor in low‐density plasmas: the empirical estimate indicates ~0.7 shorting for long Debye lengths, but the simulations predict a shorting factor of ~0.94. This paper systematically steps through the empirical and modeling methods leading to the disagreement with the intention of motivating further study on the topic.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".