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Record W2749718799 · doi:10.1002/jrs.5222

Chemical and spectroscopic investigations of <scp>K‐H<sub>3</sub>O‐Na</scp> jarosite solid solutions applicable for Mars explorations

2017· article· en· W2749718799 on OpenAlexaff
Fengke Cao, Zongcheng Ling, Yuheng Ni

Bibliographic record

VenueJournal of Raman Spectroscopy · 2017
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsWestern University
FundersNational Natural Science Foundation of China
KeywordsJarositeMars Exploration ProgramRaman spectroscopyAnalytical Chemistry (journal)Infrared spectroscopyChemistryHydrothermal circulationSulfateFerricMineralMineralogyInorganic chemistryGeologyAstrobiologyPhysicsEnvironmental chemistryOptics

Abstract

fetched live from OpenAlex

Jarosite is the first definitely discovered ferric sulfate mineral on Mars, indicating a highly acidic environment in Martian history. K‐H 3 O and Na‐H 3 O jarosite solid solutions were synthesized under hydrothermal conditions. Phase identifications and chemical compositions were determined by X‐ray diffraction and scanning electron microscopy coupled with energy dispersive spectroscopy. Raman spectra of those two series of jarosite solid solutions show systematic spectral changes with varying alkali content. When chemical ratios of K/(K + H 3 O), in K‐H 3 O jarosite solid solutions, increase from 0% to 88%, their Raman peaks exhibit systematic shifts: ν 2 (SO 4 ) 2− (from 424.7 to 434.3 cm −1 ), ν 4 (SO 4 ) 2‐ (from 619.6 to 623.8 cm −1 ), ν 1 (SO 4 ) 2‐ (from 1,011.8 to 1,006.1 cm −1 ) and, ν 3 (SO 4 ) 2− (from 1,165.4 to 1,152.9 cm −1 ). Near‐infrared and mid‐infrared spectra were also collected for the spectral library of Mars remote‐sensing studies. The detailed chemical and spectroscopic studies of K‐H 3 O‐Na jarosite solid solutions would contribute to their potential discoveries on Mars by future Mars missions (e.g., Mars 2020 and ExoMars). Copyright © 2017 John Wiley &amp; Sons, Ltd.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score0.744

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.024
GPT teacher head0.263
Teacher spread0.239 · 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 designBench or experimental
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

Citations8
Published2017
Admission routes1
Has abstractyes

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