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Record W1500513968

Integrated Spectroscopic Studies of Anhydrous Sulfate Minerals

2005· article· en· W1500513968 on OpenAlexaff
M. D. Lane, J. L. Bishop, M. D. Dyar, E. A. Cloutis, Ferenc L. Forray, T. Hiroi

Bibliographic record

VenueAbstracts of Papers Submitted to the Lunar and Planetary Science Conference · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsAnhydrousWeatheringJarositeSulfateMineralMineralogyGeologyClay mineralsChemistryGeochemistry
DOInot available

Abstract

fetched live from OpenAlex

Sulfates have been identified in Martian soils and bedrock and are emerging as an important indicator for aqueous activity on Mars. Sulfate minerals can form in a variety of low-temperature (evaporitic; chemical-weathering) and high-temperature (volcanic/fumarolic; hydrothermal) environments and their formational environments can range from alkaline to acidic. Although sulfates generally form in the presence of water, not all sulfates are hydrous or contain water in their structures. Many of these anhydrous sulfates (Dana group 28; Strunz class 67A) are minerals that form as accompanying phases to the main minerals in ore deposits or as replacement deposits in sedimentary rocks. However, some form from thermal decomposition of OH or H2O-bearing sulfates, such as from the reaction [1]: jarosite = yavapaiite + Fe2O3 + H2O. Where known, the stability fields of these minerals all suggest that they would be stable under martian surface conditions [2]. Thus, anhydrous sulfate minerals may contribute to martian surface mineralogy, so they must be well-represented in spectral libraries used for interpretation of the Martian surface. We present here the preliminary results of an integrated study of emittance, reflectance, and Mossbauer spectroscopy of a suite of wel-lcharacterized anhydrous sulfates.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.679
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

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

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 head0.027
GPT teacher head0.259
Teacher spread0.232 · 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 teacher head, 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

Citations10
Published2005
Admission routes1
Has abstractyes

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