Bibliographic record
Abstract
Abstract Iron‐bearing minerals are a major component of materials on the lunar surface, and many of them can be distinguished based on the diagnostic absorption features in visible and near‐infrared reflectance spectra. The relationship between the 1 µm absorption (Band I) center and the band area ratio (BAR), defined as the ratio of 2–1 µm absorption areas, provides a sensitive way to estimate the relative abundance of olivine (Ol) and pyroxene. In the plot of the BAR value versus the Band I center, the Ol‐orthopyroxene (Opx) mixing line (derived from terrestrial materials) is strictly applicable only to Ol‐Opx mixtures. Based on published database of laboratory spectra and compositional data for lunar rocks and mineral separates, this study investigated the spectral characteristics of a variety of common Fe2+‐bearing lunar minerals and rocks, such as clinopyroxene (Cpx), ilmenite‐rich basalt, pyroxene‐bearing anorthosite, and glass‐rich impact melt. The lunar Ol‐Cpx‐Opx mixing line for rocks and minerals is determined, which contains less curvature than the Ol‐Opx mixing line, consistent with both the higher Fe2+ content of lunar mafic silicates and the presence of appreciable Cpx. This study suggests that some of the pyroxene‐bearing lunar materials that are rich in ilmenite, glass, or plagioclase can also be distinguished by this method. These results indicate that the plot of the BAR value versus the Band I center is a useful tool for spectral analysis of lunar composition and mineralogy, especially for those having pyroxene‐dominated spectra.
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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".