The Utility of RGB Color for Discrimination of Lunar Maturity and Composition
Bibliographic record
Abstract
Abstract We explore the extent to which red‐green‐blue (RGB) color images, such as those produced by a complementary metal oxide semiconductor (CMOS) Bayer‐filter camera, can be utilized for studying the maturity and composition of the lunar surface. RGB filters typically are quite broad, with considerable overlap among the three colors. Convolution of laboratory spectra for lunar samples to the RGB responsivities of the Chang'E‐3 rover's Panoramic Camera allowed determination of the correlations between color ratios (B/R, B/G, and G/R) and the sample maturity (Is/FeO) or composition (wt.% FeO or TiO2). In general, color ratios decrease as Is/FeO increases. When separate sample categories are considered, we find that the B/R ratio is a good predictor of Is/FeO for low‐Ti mare, low‐Fe highland, and moderate‐Fe highland soils. For high‐Ti mare soils, Is/FeO has little influence on the B/R ratio (due to the spectral effects of abundant ilmenite), and hence the ratio cannot be used to determine maturity. We also find that color ratios have no useful correlation with sample wt.% FeO or TiO2. Thus, in locations excluding the high‐Ti maria, RGB color data could be used to estimate soil maturity. We outline a concept for a multispectral imager based on a CMOS sensor with a Bayer‐like pattern of custom‐wavelength filters chosen specifically for lunar science applications.
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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 | 0.001 |
| 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".