A first comparison between ionospheric and surface level magnetic fields at Mars
Bibliographic record
Abstract
With both the Mars Atmosphere and Volatile Evolution (MAVEN) mission and the Interior Exploration using Seismic Investigations, Geodesy and Heat Transport (InSight) mission concurrently operating at Mars, we are able to make two point comparisons of the vector magnetic field at Mars for the first time. During MAVEN overflights of the InSight landing site, we compared deviations in the ionospheric magnetic field to variations in the surface level magnetic field. We find significant orbit to orbit variability in the magnitude and direction of the ionospheric magnetic field as well as significant day to day variability of the surface level magnetic field. We attribute this variability to time varying ionospheric currents. However, when analyzing the ensemble of 16 individual MAVEN overflights of the InSight landing location, we see no clear correlation between the magnitudes or directions of the ionospheric magnetic field and the surface magnetic field as might be expected. If the presumed ionospheric currents have a small scale size, then the ionospheric magnetic field will display increased variability as MAVEN flies through the current structure. Whereas the present analysis is restricted to mostly nightside MAVEN overflights where current are expected to be weak, future analyses should incorporate dayside overflights where current are expected to be stronger and current signatures more clear.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".