Spectral reflectance‐compositional properties of spinels and chromites: Implications for planetary remote sensing and geothermometry
Bibliographic record
Abstract
Abstract— Reflectance spectra of spinels and chromites have been studied as a function of composition. These two groups of minerals are spectrally distinct, which relates largely to differences in the types of major cations present. Both exhibit a number of absorption features in the 0.3–26 μm region that show systematic variations with composition and can be used to quantify or constrain certain compositional parameters, such as cation abundances, and site occupancies. For spinels, the best correlations exist between Fe2+ content and wavelength positions of the 0.46, 0.93, 2.8, Restrahelen, 12.3, 16.2, and 17.5 μm absorption features, Al and Fe3+ content with the wavelength position of the 0.93 μm absorption feature, and Cr content from the depth of the absorption band near 0.55 μm. For chromites, the best correlations exist between Cr content and wavelength positions of the 0.49, 0.59, 2, 17.5, and 23 μm absorption features, Fe2+ and Mg contents with the wavelength position of the 1.3 μm absorption feature, and Al content with the wavelength position of the 2 μm absorption feature. At shorter wavelengths, spinels and chromites are most readily distinguished by the wavelength position of the absorption band in the 2 μm region (<2.1 μm for spinels, >2.1 μm for chromite), while at longer wavelengths, spectral differences are more pronounced. The importance of being able to derive compositional information for spinels and chromites from spectral analysis stems from the relationship between composition and petrogenetic conditions (pressure, temperature, oxygen fugacity) and the widespread presence of spinels and chromites in the inner solar system. When coupled with the ability to derive compositional information for mafic silicates from spectral analysis, this opens up the possibility of deriving petrogenetic information for remote spinel‐ and chromite‐bearing targets from analysis of their reflectance 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 | 0.001 |
| 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) | 0.001 | 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".