A New Method for Simulation of Lunar Microwave Brightness Temperatures and Evaluation of Chang'E‐2 MRM Data Using Thermal Constraints From Diviner
Bibliographic record
Abstract
Abstract We used the bolometric brightness temperatures (T Bol ) derived from the Lunar Reconnaissance Orbiters Diviner Lunar Radiometer (Diviner) as an upper boundary condition in our thermal model. We then calculated temperature profiles at any local time based on our improved thermal model at low to middle latitudes (70° N/S). Based on the temperature profiles, we modeled the midnight brightness temperature at 19.35 (T B19 ) and 37 GHz (T B37 ). Comparing to the Chang'E‐1 and Chang'E‐2 (CE‐1/2) observations, we found that CE‐1 showed a better data quality than that of CE‐2, especially for the T B37 data. Assuming that the issue with the CE‐2 data is caused by heat contamination of the cold‐reference antennas, we performed an empirical normalization of the CE‐2 microwave radiometer data near midnight following the approach of Hu et al. (2017). The results show that T B difference (modeled values minus modified T B ) for 19.35 GHz is less than 3.40 K for ∼80% of the pixels. At 37 GHz, ∼67% of the pixels have T B difference less than 2.88 K. Additionally, we identified some areas of low microwave temperature in our modified T B maps. These low‐T B features can be characterized by two types: (1) low T B spots at fresh craters with high rock abundance and bright rays and (2) high‐Ti lunar mare surfaces with a low content of rock fragments. Investigating these low‐T B regions with the modified T B data can reveal more information about subsurface thermal regime and properties and help us better understand the evolution of regolith on the Moon.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| 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 teacher head, 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".