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Spectral reflectance‐compositional properties of spinels and chromites: Implications for planetary remote sensing and geothermometry

2004· article· en· W2045284966 on OpenAlexaff
E. A. Cloutis, J. M. Sunshine, R. V. Morris

Bibliographic record

VenueMeteoritics and Planetary Science · 2004
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsWavelengthAbsorption (acoustics)Content (measure theory)Absorption bandChemistryAnalytical Chemistry (journal)Composition (language)Absorption spectroscopySpectral lineMineralogyMaterials scienceOpticsPhysicsEnvironmental chemistryOptoelectronics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.236
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations143
Published2004
Admission routes1
Has abstractyes

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