Compositional Maps of the Lunar Polar Regions Derived from the Kaguya Spectral Profiler and the Lunar Orbiter Laser Altimeter Data
Bibliographic record
Abstract
Abstract Due to the challenging illumination conditions of the lunar polar regions, mineralogic maps have generally been constrained to within 0°–70°N/S. Here we generate a gridded reflectance data cube from the Kaguya Spectral Profiler measurements for each polar region and calibrate it to absolute reflectance using data from the Lunar Orbiter Laser Altimeter. We use this data set to derive the first quantitative mineral maps of iron oxide (FeO), the optical maturity parameter (OMAT), and nanophase iron poleward of 50°N/S at a spatial resolution of 1 km pixel–1. We evaluate potential latitudinal trends in space weathering and optical maturity and characterize the polar geology, with an emphasis on the Artemis region (84°–90°S). The maps of FeO are in excellent agreement with the abundances measured by the Lunar Prospector and provide an increased level of detail, such as the excavation of high- and low-FeO material by De Forest crater. The OMAT maps offer a fantastic view of both polar regions. They highlight small fresh craters, walls, and central peaks, as well as rays extending through multiple degrees of latitude, such as those from Tycho and De Forest, which extend into the Artemis region. Both polar regions are characterized by the ubiquitous presence of noritic anorthosites and anorthositic norite. Low-calcium pyroxene is largely the dominant mafic mineral present. The Artemis region has relatively homogeneous FeO and plagioclase content at the observed spatial resolution. The lowest FeO values are found near Shackleton, between Shoemaker and Faustini, and on the central peak of Amundsen 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 | 0.002 | 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".