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Record W4392685389 · doi:10.1190/iceg2023-024.1

3D imaging of Mars’ shallow subsurface with orbital radar data

2024· article· en· W4392685389 on OpenAlexaff
F. J. Foss, N. E. Putzig, M. R. Perry, G. A. Morgan, A. T. Russell, B. A. Campbell, Stewart A. Levin, J. W. Holt, M. S. Christoffersen, I. B. Smith

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsYork University
Fundersnot available
KeywordsGeologyMars Exploration ProgramOrbiterGround-penetrating radarRadarRemote sensingExploration of MarsDepth soundingAstrobiologyAerospace engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Since November 2006, the Shallow Radar (SHARAD) aboard the U.S. National Aeronautics and Space Administration’s (NASA’s) Mars Reconnaissance Orbiter (MRO) has been conducting subsurface sounding operations from orbit around Mars. This extended campaign has provided tens of thousands of radar profiles of Mars shallow subsurface, with coverage density in some regions having become sufficient for performing three-dimensional (3D) imaging. Adapting methods and tools used to produce, analyze, and interpret terrestrial seismograms, we have produced and studied fully imaged 3D radargrams in Mars’ polar and mid-latitudes regions. In this report, we provide some background on the SHARAD instrument, summarize the methods and tools used in creating 3D radargrams from SHARAD data, and present example views from the latest 3D radargram in the north polar region known as Planum Boreum (PB).

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)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.237
Teacher spread0.217 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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