Aqueous Alteration as an Origin of Martian Magnetization
Bibliographic record
Abstract
Abstract Strong magnetic fields have been measured from orbit around Mars over parts of the ancient southern highlands crust and on the surface at the InSight landing site. The geological processes that are responsible for generating strong magnetization within the crust remain poorly understood. One possibility is that intense aqueous alteration of crustal materials, through the process of serpentinization, could have produced magnetite that was magnetized in the presence of a global core‐generated magnetic field. Here, we test this idea with geophysical and geochemical models. We first determine the magnetizations required to account for the observed magnetic field strengths and then estimate the amount of magnetite necessary to account for these magnetizations. For the strongest orbital magnetic field strengths, about 7 wt% magnetite is required if the magnetic layer is 10 km thick. For the surface field strength observed at the InSight landing site, 0.4–1.1 wt% magnetite is required if the magnetic layer corresponds to one or more of the three crustal layers observed in the InSight seismic data (with thicknesses from 8 to 39 km). We then investigate the minerals that are produced by aqueous alteration for various possible crustal compositions and water‐to‐rock ratios using a thermodynamic model. Magnetite abundances up to 6 wt% can be generated for dunitic compositions that could account for the strongest magnetic anomalies. For more representative basaltic starting compositions, however, more than 0.4 wt% can only be generated when using high water‐to‐rock ratios, which could account for the weaker magnetizations beneath the InSight landing site.
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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 | 0.000 | 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".