Dust cover on Curiosity's Mars Hand Lens Imager (MAHLI) calibration target: Implications for deposition and removal mechanisms
Bibliographic record
Abstract
We assessed dust coverage on the Mars Science Laboratory Mars Hand Lens Imager (MAHLI) and Alpha Particle X-Ray Spectrometer (APXS) calibration targets from 14 MAHLI images acquired at <100 μm/pixel, between Mars Science Laboratory Curiosity rover sols 34 and 2248, in order to place constraints on dust accumulation and removal on two endmember orientations (vertical and horizontal). Dust coverage was estimated by (1) determining reflectance ranges for image pixels covered by dust; (2) using the ratio of calibrated MAHLI red band data to blue band data as a proxy for the concentration of dust on the calibration target; and (3) manually counting dust-covered pixels in representative areas of the MAHLI calibration target. The results of each method are consistent within uncertainties, but the reflectance method provided the most efficient and effective way to measure dust cover on each target. Mean and median dust coverage is ~4.7% and 3.9% for the MAHLI target, 9.4% and 8.9% for the APXS target, and 51.9% and 63.4% for the REMS UV sensor. Maximum dust coverage (during the dust storm) is 49% and 42% for the MAHLI and APXS calibration targets respectively, and 80% for the REMS UV sensor. In modeling dust accumulation and removal, the best fit for the MAHLI and APXS targets is one that assumes 2–4% dust removal per sol and 20–40% efficiency in collection, while for the REMS UV sensor, it is one that assumes near 0 removal and 7% of nominal accumulation. Results indicate that the vertically-mounted MAHLI and APXS targets accumulated less dust overall during the mission than horizontally-mounted hardware such as the REMS UV sensor; this was true even during the 2018 global dust event. In addition, while the vertical orientation did not protect the targets from dust deposition during that event, dust removal following the event was more effective on these targets than on horizontally-mounted hardware. Because conditions cannot be monitored continuously, these studies cannot fully discriminate among the potential causes of this dust removal. However, the results suggest that vertical mounting is a reasonable dust mitigation strategy for hardware for which short-term dust accumulation is not a risk factor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".