An improved 50‐degree spherical harmonic model of the magnetic field of Mars derived from both high‐altitude and low‐altitude data
Bibliographic record
Abstract
This paper presents a 50‐degree spherical harmonic model of the magnetic field of Mars derived from both low‐ and high‐altitude magnetic data through covariance analysis. Three sets of models are first calculated by using the high‐altitude data alone, which include harmonics of degree 55, 60, and 65. Each set consists of four models, depending on whether the north‐south and west‐east components data are used in a model in addition to the radial component. The models correlate strongly over harmonics of degree up to about 50, except the model that is calculated by using the radial component data alone, which shows appreciable differences from the others. The high‐altitude models are then correlated with the corresponding models that were previously derived from the low‐altitude data. The correlation between the corresponding models is again very high over harmonics of degree 4–50, confirming the reliability of the spherical harmonic coefficients over these harmonic degrees. The slightly lower correlation, ∼0.7, over harmonics of degree lower than 4 implies some contribution from quasi‐steady external field. A final 50‐degree model is determined from the covarying harmonics of the low‐ and high‐altitude models that use all three components of the magnetic data. The corresponding radial and tangential components of the magnetic anomalies are presented at the surface of Mars, along with their error limits.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".