Lunar central peak mineralogy and iron content using the Kaguya Multiband Imager: Reassessment of the compositional structure of the lunar crust
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Bibliographic record
Abstract
Ryder and Wood (1977) suggested that the lunar crust becomes more mafic with depth because the impact melts associated with the large Imbrium and Serenitatis basins are more mafic than the surface composition of the Moon. In this study, we reexamine the hypothesis that the crust becomes more mafic with depth; we analyze the composition of crater central peaks by using recent remote sensing data and combining the best practices of previous studies. We compute the mineralogy for 34 central peaks using (1) nine-band visible and near-infrared data from the Kaguya Multiband Imager, (2) an improved version of Hapke's radiative transfer model validated with spectra of lunar soils with well-known modal mineralogy, and (3) new crustal thickness models from the Gravity Recovery and Interior Laboratory data to examine the variation in composition with depth. We find that there is no increase in mafic mineral abundances with proximity to the crust/mantle boundary or with depth from the current lunar surface and, therefore, that the crust does not become more mafic with depth. We find that anorthosite with very low mafic abundance (“purest anorthosite” or PAN) is a minority constituent in these peaks, and there is no clear evidence of a distinct PAN-rich layer in the middle crust as previously proposed. The composition of most of the central peaks we analyze is more mafic than classically defined anorthosites with an average noritic anorthosite composition similar to that of the lunar surface.
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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 | 0.000 | 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 it