Global Regolith Thermophysical Properties of the Moon From the Diviner Lunar Radiometer Experiment
Bibliographic record
Abstract
Abstract We used infrared data from the Lunar Reconnaissance Orbiter (LRO) Diviner Lunar Radiometer Experiment to globally map thermophysical properties of the Moon's regolith fines layer. Thermal conductivity varies from 7.4 × 10−4 W m−1 K−1 at the surface to 3.4 × 10−3 W m−1 K−1 at depths of ~1 m, given density values of 1,100 kg m−3 at the surface to 1,800 kg m−3 at 1 m depth. On average, the scale height of these profiles is ~7 cm, corresponding to a thermal inertia of 55 ± 2 J m−2 K−1 s−1/2 at 273 K, relevant to the diurnally active near‐surface layer, ~4–7 cm. The temperature dependence of thermal conductivity and heat capacity leads to an ~2 times diurnal variation in thermal inertia at the equator. On global scales, the regolith fines are remarkably uniform, implying rapid homogenization by impact gardening of this layer on timescales <1 Gyr. Regional‐ and local‐scale variations show prominent impact features <1 Gyr old, including higher thermal inertia (> 100 J m−2 K−1 s−1/2) in the interiors and ejecta of Copernican‐aged impact craters and lower thermal inertia (< 50 J m−2 K−1 s−1/2) within the lunar cold spots identified by Bandfield et al. (2014). Observed trends in ejecta thermal inertia provide a potential tool for age dating craters of previously unknown age, complementary to the approach suggested by Ghent et al. (2014). Several anomalous regions are identified in the global 128 pixels per degree maps presented here, including a high‐thermal inertia deposit near the antipode of Tycho crater.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".