Atmospheric dynamics at the Phoenix landing site as seen by the Surface Stereo Imager
Bibliographic record
Abstract
The Surface Stereo Imager has made observations of dust blowing aloft and clouds near the horizon at the Phoenix landing site. These subtle features are apparent because of the high signal‐to‐noise ratio of the camera which allows for the removal of a mean frame from multiple images captured in rapid succession and the ability to conduct simultaneous capture through different filters in each camera eye. By examining the ratios between two filters, it was possible to determine in a relative sense how the water ice content of the atmosphere changed over the mission and on a diurnal time scale. The direction of travel and speed of features aloft near the zenith has been inferred and agree well with the diurnal pattern of near‐surface wind direction from the Telltale. Direct observation of cumulus‐like cloud near the surface suggests convection of water vapor–rich air, but only until midday, requiring a mechanism to inhibit cloud formation in the early afternoon. The spectral ratios agree well with the observation of cloud and indicate a general increase in water ice toward the end of the mission as well as a strong diurnal pattern. However, even in periods of high water ice content, there is still a great deal of variability and days when dense clouds are absent. Also, different cloud layers are occasionally observed moving in different directions, indicating occasional wind shear aloft. Features observed had estimated minimum optical depths up to 0.11.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".