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Record W2382497931 · doi:10.1111/maps.12646

The oxidation state of nanophase Fe particles in lunar soil: Implications for space weathering

2016· article· en· W2382497931 on OpenAlexfundno aff
M. S. Thompson, T. J. Zega, P. Becerra, J. T. Keane, Shane Byrne

Bibliographic record

VenueMeteoritics and Planetary Science · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Aeronautics and Space Administration
KeywordsSpace weatheringWeatheringLunar soilOxidation stateNanoparticleMaterials scienceSoil waterTransmission electron microscopyRegolithElectron energy loss spectroscopyMaturity (psychological)SpectroscopyOxygenAnalytical Chemistry (journal)MineralogyChemical engineeringAstrobiologyEnvironmental chemistryChemistryGeologyMetallurgyNanotechnologyMetalSoil scienceGeochemistry

Abstract

fetched live from OpenAlex

Abstract We report measurements of the oxidation state of Fe nanoparticles within lunar soils that experienced varied degrees of space weathering. We measured >100 particles from immature, submature, and mature lunar samples using electron energy‐loss spectroscopy ( EELS ) coupled to an aberration‐corrected transmission electron microscope. The EELS measurements show that the nanoparticles are composed of a mixture of Fe 0 , Fe 2+ , and Fe 3+ oxidation states, and exhibit a trend of increasing oxidation state with higher maturity. We hypothesize that the oxidation is driven by the diffusion of O atoms to the surface of the Fe nanoparticles from the oxygen‐rich matrix that surrounds them. The oxidation state of Fe in the nanoparticles has an effect on modeled reflectance properties of lunar soil. These results are relevant to remote sensing data for the Moon and to the remote determination of relative soil maturities for various regions of the lunar surface.

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 score0.001
Threshold uncertainty score0.002

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)0.0000.000

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 head0.017
GPT teacher head0.244
Teacher spread0.227 · 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

Citations96
Published2016
Admission routes1
Has abstractyes

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