A Record of Water-ice Clouds at the Phoenix Landing Site Derived from Modeling MET Temperature Data
Bibliographic record
Abstract
Abstract Water-ice clouds were frequently detected throughout the 151-sol Phoenix mission by the Phoenix lidar, providing insight into the Martian water cycle. However, the lidar could not be used continuously, and as such, the cloud data were temporally constrained to when observations were acquired. Here we reconstruct a record of water-ice clouds at the Phoenix landing site by examining the radiative contribution made by the clouds to the surface energy balance. This is accomplished by modeling the data from the 2 m MET air temperature sensor on board the lander. Clouds radiating from 0 and 30 W m−2 of energy toward the surface are consistent with the MET record over the course of the mission. The additional longwave flux at the surface induced a warming of the surface and near-surface temperatures, usually between 1–3 K; however, the clouds showed a high degree of sol-to-sol variability. This radiative analysis indicates that clouds were present much earlier in the mission than previously known, and cloud emission reached a maximum near sol 90, consistent with analyses of the annular cloud at the Phoenix landing site. The modeled flux from clouds was compared to the water-ice optical depth retrieved from the Phoenix lidar, showing that optically thicker clouds emitted more radiation toward the surface.
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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 | 0.000 | 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 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".