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Record W2033305154 · doi:10.1029/2012je004053

The dielectric permittivity of terrestrial ground ice formations: Considerations for planetary exploration using ground‐penetrating radar

2012· article· en· W2033305154 on OpenAlexaffabout
Laura Thomson, G. R. Osinski, Wayne H. Pollard

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsMcGill UniversityUniversity of OttawaWestern University
FundersEurostars
KeywordsGround-penetrating radarGeologyMars Exploration ProgramPermittivityGeophysicsDielectric permittivityMartianDielectricGeomorphologyLandformArcticPermafrostRadarAstrobiologyMaterials scienceOceanographyPhysicsAerospace engineering

Abstract

fetched live from OpenAlex

Exploration of the polar ice caps and apparent glacial and periglacial landforms on Mars will aid our understanding of its ancient climate conditions and the history of water on the planet. Given that ground‐penetrating radar (GPR) is likely to be used to understand these features, we investigated the real component of the complex dielectric permittivity of stratified segregation ice, non‐stratified segregation ice, and polygonal ice wedge deposits in the Canadian Arctic. We acquired moveout profiles with a 450 MHz GPR on ground ice formations that had active layer sediments excavated prior to surveying. Using ice core data collected from these sites, we found that the volumetric fraction of ice plays the greatest role in defining the dielectric permittivity of the deposit and that it can be described using a modified complex refractive index method (CRIM) dielectric mixing model. Using the modified CRIM model, we estimate the dielectric permittivity of several ground ice deposits on Earth and present further estimates for similar features on Mars using permittivity values for Martian sediments derived from both theory and laboratory methods.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.003
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 head0.113
GPT teacher head0.346
Teacher spread0.234 · 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 designTheoretical or conceptual
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

Citations15
Published2012
Admission routes2
Has abstractyes

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