The Mastcam-Z Radiometric Calibration Targets on NASA’s Perseverance Rover: Derived Irradiance Time-Series, Dust Deposition, and Performance over the First 350 Sols on Mars.
Bibliographic record
Abstract
The Mastcam-Z radiometric calibration targets mounted on the NASA’s Perseverance rover proved to be effective in the calibration of Mastcam-Z images to reflectance (I/F) over the first 350 sols on Mars. Mastcam-Z imaged the calibration targets regularly to perform reflectance calibration on multispectral image sets of targets on the Martian surface. For each calibration target image, mean radiance values were extracted for 41 distinct regions of the targets, including patches of color and grayscale materials. Eight strong permanent magnets, placed under the primary target, attracted magnetic dust and repelled it from central surfaces, allowing the extraction of radiance values from eight regions relatively clean from dust. These radiances were combined with reflectances obtained from laboratory measurements, a one-term linear fit model was applied, and the slopes of the fits were retrieved as estimates of the solar irradiance and used to convert Mastcam-Z images from radiance to reflectance. Derived irradiance time series are smoothly varying in line with expectations based on the changing Mars-Sun distance, being only perturbed by a few significant dust events. The deposition of dust on the calibration targets was largely concentrated on the magnets, ensuring a minimal influence of dust on the calibration process. The fraction of sunlight directly hitting the calibration targets was negatively correlated with the atmospheric optical depth, as expected. Further investigation will aim at explaining the origin of a small offset observed in the fit model employed for calibration, and the causes of a yellowing effect affecting one of the calibration targets materials.
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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 | 0.001 | 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 | 0.000 |
| 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".