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Record W7112508575

Radar et Géomorphologie Planétaire dans la Cryosphère: Investigation des Systemes Glaciaires-Periglaciaires dans Phlegra Montes, Mars et le Territoire du Yukon, Canada

2025· other· en· W7112508575 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsGlacierPermafrostMars Exploration ProgramGlacial periodCryosphereLandformGlacial landform
DOInot available

Abstract

fetched live from OpenAlex

Mars has abundant water-ice across its surface and in the subsurface, particularly in the form of glacier and permafrost ice. These cryosphere elements drive the development of numerous landforms that are also observed on Earth, enabling comparative studies between both planets. In particular, the mid-latitude region of Mars (30-50°N) is where thousands of viscous flow features (VFFs) also called debris-covered glaciers, are situated. In this PhD thesis, I use a combination of orbital radar sounding, high-resolution imagery, topographic data, and geomorphological techniques to analyze glacial landforms in the Phlegra Montes region of Mars. This study is supported by geophysical field investigations in the Canadian Arctic, a well-established analogue for Martian permafrost and glacial landscapes. Together, these approaches provide new insights into the structure, dynamics, and detection of shallow ice systems on Mars and Earth. Three major findings are presented. First, I report the discovery of a debris-covered glacier with terraced topography as well as a hanging glacier in Phlegra Montes, Mars detected using SHARAD (radar) data, representing the first known observation of such features on Mars. Secondly, I provide evidence supporting past interpretations that VFFs and the latitude- dependent mantle (LDM) are separate, yet overlapping systems. Lastly, through field-based radar and sedimentological surveys in Tombstone Territorial Park, I identify the most suitable radar frequency range for detecting shallow subsurface ice (1–5 m), offering direct guidance for future Mars missions using radar instrumentation. These findings enhance our understanding of Mars' cryosphere and its recent glacial history. They also support mission planning efforts for future exploration and subsurface ice mapping, for example and/or including the International Mars Ice Mapper (I-MIM) and Mars Life Explorer (MLE) missions.

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: Observational
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.078
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.152
Teacher spread0.145 · 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
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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