CHARACTERIZATION OF NATURAL FELDSPARS BY RAMAN SPECTROSCOPY FOR FUTURE PLANETARY EXPLORATION
Bibliographic record
Abstract
The Raman spectra of a large number of natural feldspar-group minerals were obtained to determine what compositional and structural information can be inferred solely from their Raman spectra. The feldspar minerals selected cover a wide range of Na, K, and Ca proportions, crystal structures and degrees of cation disorder. The samples include both homogeneous feldspar phases and a few with visible intergrowths. From the positions of the strongest Raman peak in the spectrum, four structural types of feldspars can be readily identified: orthoclase (and microcline), albite, high-temperature plagioclase, and anorthite. Using a Raman spectral database of feldspar minerals established during this study and an autonomous spectral search-and-match routine, up to seven different types of feldspar can be unambiguously determined. Three additional feldspar types can be further resolved by careful visual inspection of the Raman spectra. We conclude that ten types of feldspars can be classified according to their structure, crystallinity, and chemical composition solely on the basis of their Raman spectra. Unlike olivine, pyroxene and some Fe-oxides, the Raman peak positions of the feldspars cannot be used to extract quantitative information regarding the cation composition of the feldspar phases. We also define the necessary specifications of a field Raman spectrometer capable of characterizing feldspar minerals during planetary surface exploration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".