Dusty Rocks in Gale Crater: Assessing Areal Coverage and Separating Dust and Rock Contributions in APXS Analyses
Bibliographic record
Abstract
Abstract A thin, patchy layer of airfall dust covers rock surfaces examined by the Mars Science Lab rover Curiosity and complicates interpretation of textures in Mars Hand Lens Imager images and compositions determined by Alpha Particle X‐ray Spectrometer (APXS). Using three image processing methods, we estimate dust coverages for Mars Hand Lens Imager images of APXS targets to Sol 1512. Dust coverages of as is rock targets range from 6% to 77% (±5% to 10% estimated error). Targets brushed by the Dust Removal Tool range to lower coverages than as is targets, but quality depends on surface type; brushed mudstones have the narrowest range and lowest coverages (11–25%), while sandstones vary, ranging to higher coverages (12–58%). Groups of rocks with similar compositions (APXS classes) can have strong correlations between dust coverage and SO3/Cl (up to r = 0.985). Dust can also strongly affect the lightest elements measured (Na to Ca). By comparing paired as is and Dust Removal Tool analyses, using the determined dust coverages, and finding a best fit dust thickness (generally ~10 μm), we model relative contributions of the dust and bedrock to extrapolate dust‐free compositions for homogeneous APXS classes. The dust is basaltic with high S and Cl. Dust‐free rocks have higher SiO2 and Na2O (up to 6.5 wt% and 0.5 wt% higher, respectively) and lower SO3 and CaO (up to 5.5 wt% and 1.3 wt% lower, respectively) than dusty equivalents. Dust most influences compositions that are very different from average Mars, including the alkali‐rich, MgO‐poor Jake M class.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".