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Record W4283158853 · doi:10.5194/icg2022-282

Relationship between thermal-contraction polygons and substrate properties on Mars

2022· preprint· en· W4283158853 on OpenAlexaff
Meven Philippe, Susan J. Conway, R. J. Soare, Lauren E. McKeown

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsDawson College
Fundersnot available
KeywordsMars Exploration ProgramPolygon (computer graphics)GeologyWedge (geometry)GroundwaterGeometryPorosityGeomorphologyGeophysicsAstrobiologyGeotechnical engineeringPhysicsMathematicsEngineering

Abstract

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On Earth, sharp drops in negative temperatures can cause ice-cemented ground to crack and form polygonal patterns. Over time, water and/or sand can infill cracks (Péwé, 1959; Lachenbruch, 1962; Black, 1976). This material can freeze into ice or sand wedges, uplifting polygon margins and forming low-centred polygons (LCPs). When wedges degrade, elevation of margins decreases which forms high-centred polygons (HCPs). On Mars, similar polygonally-patterned ground is observed (with LCPs and HCPs) and also thought to be formed by thermal contraction of the ground (Mellon, 1997), but the type of wedge is unknown. Liquid water is thought to have been unstable on Mars’s surface for 3 billion years, but a recent study in Utopia Planitia suggested an ice-wedge origin for the studied polygons (Soare et al., 2021). This implies near-surface liquid water on Mars in the recent past, and presence of massive ice in the subsurface – an interesting source of water for future manned missions. Here, we investigate the relationship between polygon density & type and the properties of the substrate that bears them (e.g. grain size or porosity). We focus on polygons in Utopia Planitia and use the same grid-based mapping technique as Soare et al. (2021). This technique consists in gridding the study area in squares of given dimensions (500 x 500 m), and in each square noting the presence of each polygon type. We mapped three geomorphological units in our study area: the “sinuous unit” (sinuous shape, polygon-rich), the “boulder unit” (covered in decametric boulders, polygon-poor), and the craters. For each unit we calculated parameters (e.g. percentage of squares containing polygons) which we expect to act as proxies for different substrate properties (e.g. capacity for the ground to form polygonally-patterned ground). We found that: the boulder unit is an ice-poor massive material hindering ground cracking / polygon formation; the sinuous unit is an ice-rich material favouring ground cracking, but not ice wedge formation or preservation; crater floors host ice-rich material favouring ground cracking, and are environments favourable to ice wedge formation and preservation. Our study area is located at the terminus of Hrad Vallis, a valley system originating from a nearby volcano (Elysium Mons) and thought to have conveyed both lava and mudflows (Hamilton et al., 2018). Therefore, we suggest that the boulder unit could be a low-viscosity lava flow, which would have been topped by a later viscous mudflow that formed the sinuous unit, both originating from Hrad Vallis. The low elevation of crater floors compared to their surroundings leads to higher atmospheric pressure and lower temperatures at their bottom. This could favour ground ice formation and preservation – a “cold trap effect” already discussed by Conway et al. (2018) and Soare et al. (2021). In summary, we show that polygon density and type can provide insights into the geological properties of a substrate, and here it allowed us to suggest origins for the units of our study zone that are consistent with the geological context of the area.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.077
GPT teacher head0.256
Teacher spread0.178 · 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".

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Citations1
Published2022
Admission routes1
Has abstractyes

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