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Record W4291433219 · doi:10.1029/2022gl099250

A Minimally Cemented Shallow Crust Beneath InSight

2022· article· en· W4291433219 on OpenAlexfundno aff
Vashan Wright, Jhardel Dasent, Richard Kilburn, Michael Manga

Bibliographic record

VenueGeophysical Research Letters · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsnot available
FundersNuclear Safety and Security CommissionCanadian Institute for Advanced ResearchNational Aeronautics and Space AdministrationEarth Sciences DivisionDivision of Earth SciencesNational Science Foundation
KeywordsGeologyMartianCrustMars Exploration ProgramBasaltMineralGeochemistryCementPetrologyAstrobiologyMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Abstract Ice and other mineral cements in Mars' shallow subsurface affect the mechanical properties of the shallow crust, the geologic processes that shape the planet's surface, and the search for past or extant Martian life. Cements increase seismic velocities. We use rock physics models to infer cement properties from seismic velocities. Model results confirm that the upper 300 m of Mars beneath InSight is most likely composed of sediments and fractured basalts. Grains within sediment layers are unlikely to be cemented by ice or other mineral cements. Hence, any existing cements are nodular or formed away from grain contacts. Fractures within the basalt layers could be filled with gas, 2% mineral cement and 98% gas, and no more than 20% ice. Thus, no ice‐ or liquid water‐saturated layers likely exist within the upper 300 m beneath InSight. Any past cement at grain contacts has likely been broken by impacts or marsquakes.

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 score0.007
Threshold uncertainty score0.014

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 communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.047
GPT teacher head0.297
Teacher spread0.250 · 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

Citations18
Published2022
Admission routes1
Has abstractyes

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