Simulating optical depth studies on a Martian helicopter via a drone-based field study in the Alvord Desert, Oregon
Bibliographic record
Abstract
Dust studies are of interest to the Martian science community as they reveal important information on the spatial and temporal variability of dust loading in the atmosphere. This dust concentration information, in turn, illuminates atmospheric circulation and boundary layer height. To monitor dust in the atmosphere on Mars, NASA’s Martian Science Laboratory rover’s Mastcam and Navigation Camera (NavCam) have probed the atmosphere in Gale Crater since its landing in 2012. To determine dust loading in the Martian atmosphere, line-of-sight (LOS) optical depth has been used in previous studies using Mastcam (e.g. Smith et al. (2019)). These studies calculate optical depth per km, a quantity known as extinction, that is directly proportional to the number of dust particles per unit volume. However, due to the MSL rover’s inability to move significantly in vertical space before dust conditions change, elevation-dependent studies are limited to elevation angle studies, resulting in the inability to constrain the spatial position of dust features (such as dust devils) and the inability to get optical depth along a constant-elevation line of sight. The goal of this work is to develop and demonstrate a framework in which the spatial variation in extinction can be observed and calculated via a camera mounted on a drone flight, which can be applied to a future drone-based Mars mission. To test this, footage from a drone-mounted camera flown in the Alvord Desert, Oregon, United States in 2024 was used to determine optical depth as a function of elevation in RGB color channels. The drone flew above a desert salt pan over the course of several minutes. Two frames from one of the drone videos, one near-ground and one elevated, is given in Figure 1. To calculate the line-of-sight optical depth in the region of interest, the mountainous region far away from the camera, a similar method to what is used in Smith et al. (2019), will be used, but will be further improved via geometric transformations to obtain a horizontal-layered altitude profile of line-of-sight optical depth. These improvements, and how they can be used in a Mars-like setting to construct a full vertical profile of line-of-sight optical depth, will be discussed in this conference presentation. ReferencesMoores et al. 2015. Icarus. https://doi.org/10.1016/j.icarus.2014.09.020Smith et al. 2019. Geophys. Res. Lett. https://doi.org/10.1029/2019GL083788
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".