The Mars Science Laboratory (MSL) Mast cameras and Descent imager: Investigation and instrument descriptions
Bibliographic record
Abstract
The Mars Science Laboratory Mast camera and Descent Imager investigations were designed, built, and operated by Malin Space Science Systems of San Diego, CA. They share common electronics and focal plane designs but have different optics. There are two Mastcams of dissimilar focal length. The Mastcam-34 has an f/8, 34 mm focal length lens, and the M-100 an f/10, 100 mm focal length lens. The M-34 field of view is about 20° × 15° with an instantaneous field of view (IFOV) of 218 μrad; the M-100 field of view (FOV) is 6.8° × 5.1° with an IFOV of 74 μrad. The M-34 can focus from 0.5 m to infinity, and the M-100 from ~1.6 m to infinity. All three cameras can acquire color images through a Bayer color filter array, and the Mastcams can also acquire images through seven science filters. Images are ≤1600 pixels wide by 1200 pixels tall. The Mastcams, mounted on the ~2 m tall Remote Sensing Mast, have a 360° azimuth and ~180° elevation field of regard. Mars Descent Imager is fixed-mounted to the bottom left front side of the rover at ~66 cm above the surface. Its fixed focus lens is in focus from ~2 m to infinity, but out of focus at 66 cm. The f/3 lens has a FOV of ~70° by 52° across and along the direction of motion, with an IFOV of 0.76 mrad. All cameras can acquire video at 4 frames/second for full frames or 720p HD at 6 fps. Images can be processed using lossy Joint Photographic Experts Group and predictive lossless compression.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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".