Martian Wind and turbulence heard by the SuperCam microphone on the perseverance rover
Bibliographic record
Abstract
On top of listening to laser shots, rover sounds and the Ingenuity rotorcraft, SuperCam’s Mars microphone has recorded over 7 hours of ambient background noise on Mars. These background recordings contain signal due to the Martian wind. Through a comparison to the meteorological data recorded by the MEDA (Mars Environmental Dynamics Analyzer), we can determine the relationships between the microphone data, the wind and the atmospheric stability. Based on these relationships, we have determined a way to estimate the wind speed using the microphone through Gaussian process regression, a machine learning technique. Owing to the sampling rate of 25 000 samples per second, the microphone data can be used to examine Mars’ atmospheric dynamics at high frequencies, as yet unexplored on Mars. We will demonstrate how the wind speed estimates from the microphone provide an assessment of turbulence at fine scales, shedding light on the dissipative regime on Mars. One particularly interesting signal recorded by the microphone was a dust devil, which had fast varying winds within the walls of its vortex and signal from dust particles hitting the rover. Combining the microphone data with information from the MEDA sensors and navigation camera (Navcam) images enabled a full parameterization of this event.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".