The observation of episodic dust storms in Martian Year 37, by the EXI camera of the Emirates Mars Mission
Bibliographic record
Abstract
The Emirates Mars Mission (EMM) started its science phase in Martian Year 36, solar longitude 49 (May 2021) [1,2]. EMM observes the Mars atmosphere and surface. These observations are unique because of the high-altitude orbit of EMM. The EMM spacecraft has the camera EXI (Emirates Exploration Imager). The EXI camera observes various dust storms on Mars [3]. The result are (sub-)hourly image sequences of dust storms. That is the basis for exploring episodic dust storms. That includes episodic dust storms in Martin Year 36 [4,5]. This conference contribution is follow-on study. The focus are episodic dust storms in Martian Year 37.We present recent EMM observations of dust storms in Martian Year 37. We select episodic dust storms for detailed study. That includes the formation and evolution of dust storms. Also, we present related study of dust storm dynamics. We explore dust storm characteristics, such as winds, surface dust lifting, and large-scale meteorology.Acknowledgments: Funding for the development of the Emirates Mars Mission (EMM) mission was provided by the UAE government. CG, BKG, NG, RMBY, and MJW would like to acknowledge EMM science management by the UAE Space Agency. CG, BKG, NG, and RMBY were supported by the UAE University (UAEU). They would like to acknowledge the Department of Physics and the Planetary Science Team of the National Space Science and Technology Center (NSSTC) in the UAEU.References: [1] Almatroushi, H., et al. (2021). Space Science Reviews, 217(8), 1-31. [2] Amiri, H. E. S., et al. (2022). Space Science Reviews, 218, 4 (2022). [3] Guha, B. K., et al. (2024). Journal of Geophysical Research: Planets, 129(4), e2023JE008156. [4] Gebhardt, C., et al. (2022). Geophysical Research Letters. 49, e2022GL099528. [5] Gebhardt, C., et al. (2023). Geophysical Research Letters, 50(24), e2023GL105317.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".