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Record W7101386170 · doi:10.1029/2024je008830

Regional Ice‐Depth and Thickness in Phlegra Montes, Mars From Radar Characterization of Glacial Landsystems Using SHARAD

2025· article· en· W7101386170 on OpenAlexafffund

Bibliographic record

VenueJournal of Geophysical Research Planets · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space Agency
KeywordsGlacierGlacial periodMars Exploration ProgramGlacial landformLandformRadarDebris flowDebrisIce stream

Abstract

fetched live from OpenAlex

Abstract Morphological analyses of Viscous Flow Features (VFFs) in the mid‐latitude regions of Mars have led to the hypothesis that these landforms are equivalent to debris‐covered glaciers. Phlegra Montes is a 1400‐km‐long mountain range in the northern mid‐latitudes that spans from 30° to 50°N, where there is an abundance of glacial landsystems thought to have a substantial amount of near‐surface ice. We identified eight VFFs that are detectable by the SHAllow RADar (SHARAD) and calculated their lag thickness, ice thickness, apparent ice depth, and volume. Phlegra Montes houses a minimum volume of ∼1.2 × 10 12 m 3 (with 8% uncertainty) of subsurface water–ice with supraglacial debris thicknesses on the order of 2–8 m across all study sites. We determine that these glaciers are composed of a material with and . We also report the first detection of a composite glacial system with undulating terraced topography detected by SHARAD. This detection consists of two subglacial terraces going from higher to lower topography. We also report the first detection of a perched or “hanging” valley glacier on the eastern flank of the mountain range.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.054
GPT teacher head0.324
Teacher spread0.270 · 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 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

Citations2
Published2025
Admission routes2
Has abstractyes

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