The Origin of Observed Magnetic Variability for a Sol on Mars From InSight
Bibliographic record
Abstract
Abstract Day‐night variations in the magnetic field at Mars have been previously observed at satellite altitudes. The InSight Fluxgate Magnetometer (IFG) has provided the first evidence for diurnal magnetic field variations at the martian surface. IFG data show diurnal variations with typical peak amplitudes of 20–40nT in the early morning to midmorning; the amplitude of the magnetic field varies over the first 389 sols of the mission and peaks between sols 50 and 100. Temperature variations, solar array currents, and lander activities all generate magnetic fields. Particularly, the first two of these also produce signals with clear diurnal variations. We first assess the IFG data calibration and conclude that temperature and solar array currents have only minimal effects on the variability we observe in the final calibrated magnetic field data. We use satellite magnetic field data and a Mars global circulation model to make predictions for the temporal evolution of wind‐driven fields in the ionosphere. Such fields vary due to seasonal changes in the ionization profile and the winds, and in the altitude range of the dynamo region, that is, the region in which electric currents can be produced. We find that the amplitude and seasonal variability of the surface magnetic fields are generally consistent with those predicted from wind‐driven currents. Moreover, a regional dust storm in the vicinity of the InSight landing site, which started around sol 45, might be responsible for the higher magnetic field amplitudes observed in the IFG data in the early part of the mission.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".