A Raman spectroscopy–compositional–structural investigation of lunar surface materials and analogues
Bibliographic record
Abstract
Abstract We investigated the Raman spectroscopy of 35 rock and mineral samples of composition relevant to the lunar surface: pyroxenes, olivine, plagioclase feldspars and oxides. The Raman spectra were analysed in the context of their compositional and structural properties to develop robust correlations to enable their detection on the lunar surface by Raman spectroscopy. We developed and implemented a spectral deconvolution program to model the fluorescence background and various Raman peaks to extract their positions in wavenumber space. Relations were developed between the Raman peak positions of pyroxenes around 325, 670 and 1000 cm −1 with their compositions in terms of enstatite, ferrosilite and wollastonite components. For olivines, we verified previously determined correlations between the positions of the olivine‐related peak doublet between 800 and 880 cm −1 with forsterite content. We also discuss the main signatures detected in the Raman spectra of plagioclases and oxides. The derived relationships were examined using Raman spectra of the matrix and several inclusions of the lunar meteorite NWA12593. We were able to identify the main endmembers of the selected surface spots as pyroxenes, plagioclase feldspars and olivine. We used the previous correlations to separate orthopyroxene from clinopyroxene signatures and to propose a composition of the inclusions of the meteorite. The results of this study demonstrate the utility of Raman spectroscopy for determining the mineralogy of the lunar 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 | 0.001 | 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) | 0.001 | 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".