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Record W2966441284

Variations in Visible/Near-Infrared Hematite Spectra Related to Grain Size and Crystallinity

2019· article· en· W2966441284 on OpenAlexaff
J. R. Johnson, E. A. Cloutis, A. A. Fraeman, J. F. Bell, Danika Wellington, B. Horgan, E. B. Rampe, D. T. Vaniman, P. Pinet

Bibliographic record

VenueLunar and Planetary Science Conference · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsHematiteMineralogyCrystallinitySpectral lineGrain sizeAmorphous solidMars Exploration ProgramFerricGeologyMaterials scienceAnalytical Chemistry (journal)ChemistryAstrobiologyPhysicsMetallurgyCrystallographyEnvironmental chemistryComposite materialGeomorphology
DOInot available

Abstract

fetched live from OpenAlex

Drill fines created by the Curiosity rover at Gale Crater, Mars have exhibited variable visible/near-infrared spectral features attributable to the presence of ferrous and ferric minerals. Drilled locations within the Murray formation and on the Vera Rubin Ridge (VRR) were shown by the CheMin instrument to contain significant amounts of hematite. However, typical hematite spectral features (e.g., absorptions near 530 nm and 860 nm) have varied inconsistently with hematite abundances. This suggests that other factors such as hematite grain size or crystallinity, the presence of amorphous materials, and/or photometric effects play a role in the observed spectra. Using laboratory spectra of hematite acquired at difference grain sizes, we document the variability in key spectral features. We also compare spectral parameters computed from Mastcam spectra on Mars of three hematite-bearing ChemCam calibration target (CCCT) samples with known hematite and amorphous material abundances.

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)0.000
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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.010
GPT teacher head0.221
Teacher spread0.211 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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