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Record W2942849662 · doi:10.1029/2018gl081339

Natural Analogue Constraints on Europa's Non‐ice Surface Material

2019· article· en· W2942849662 on OpenAlexafffund
Mark Fox‐Powell, G. R. Osinski, D. M. Applin, J. Stromberg, Fernando Gázquez, E. A. Cloutis, E. J. Allender, C. R. Cousins

Bibliographic record

VenueGeophysical Research Letters · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsUniversity of WinnipegWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationLeverhulme TrustUniversity of Winnipeg
KeywordsSulfateIcy moonAstrobiologyGeologyChlorideJupiter (rocket family)MineralogyReflectivityGeochemistryNatural (archaeology)ChemistryPlanetPaleontologyPhysicsSaturnOptics

Abstract

fetched live from OpenAlex

Abstract Non‐icy material on the surface of Jupiter's moon Europa is hypothesized to have originated from its subsurface ocean and thus provide a record of ocean composition and habitability. The nature of this material is debated, but observations suggest that it comprises hydrated sulfate and chloride salts. Analogue spectroscopic studies have previously focused on single‐phase salts under controlled laboratory conditions. We investigated natural salts from perennially cold (<0 °C) hypersaline springs and characterized their reflectance properties at 100, 253, and 293 K. Despite similar major ion chemistry, these springs form mineralogically diverse deposits, which when measured at 100 K closely match reflectance spectra from Europa. In the most sulfate‐rich samples, we find that spectral features predicted from laboratory salts are obscured. Our data are consistent with sulfate‐dominated europan non‐icy material and further show that the emplacement of endogenic sulfates on Europa's surface would not preclude a chloride‐dominated ocean.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.911
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.007

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 head0.016
GPT teacher head0.277
Teacher spread0.261 · 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; both teacher heads agree on what is shown here.

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

Citations9
Published2019
Admission routes2
Has abstractyes

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