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Record W4392577613 · doi:10.5194/egusphere-egu24-12138

Morphological comparison of polygonal patterned ground across the Arctic and Antarctic: Implications for polygon formation on Earth and Mars

2024· preprint· en· W4392577613 on OpenAlexaffabout
Jonas Eschenfelder, Cansu Culha, Shawn Chartrand, M. Jellinek

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
Fundersnot available
KeywordsPolygon (computer graphics)Mars Exploration ProgramAstrobiologyArcticGeologyThe arcticEarth (classical element)Earth scienceComputer scienceBiologyMathematicsOceanography

Abstract

fetched live from OpenAlex

Polygonal patterned ground (polygons) is ubiquitous in polar periglacial regions. It is thought to form due to repeated fracturing of the ground during freeze-thaw as a result of fluctuations in air temperature and soil conditions. Polygons can channelise overland flow in their troughs and guide groundwater flow during permafrost thaw, providing pathways for channel network development. Given the importance of polygons on local hydrology and geomorphology in cold regions, a key knowledge gap exists: We do not yet understand the evolution of existing polygons or the formation of new polygons under a changing climate. This is especially important as climate change is causing cold region water budgets to change, driving landscape change.We investigate the morphologic characteristics that are associated with polygons to indirectly examine their formation mechanism. We extensively map polygon morphologies across the McMurdo Dry Valleys in Antarctica, Prudhoe Bay in Alaska, as well as on Devon Island and Axel-Heiberg Island in the Canadian High Arctic using high-resolution DEMs derived from LiDAR data. We use a semi-automatic mapping tool based on adaptive thresholding to accelerate and scale our efforts while also improving reproducibility. We calculate surface slope and roughness for baseline lengths of 3m to 300m to investigate how and whether polygon morphology varies with local and regional topography.Overall, we quantify how polygon shape and form varies by proximity to important hydrological features. For example, in the McMurdo Dry Valleys, polygons are more often orthogonal and low-centred when they are closer to streams and glacier termini, but are characteristically hexagonal and high-centred elsewhere. Orthogonal polygons are characterised by a smoother surface compared to hexagonal polygons across all baseline lengths, bounded by a rough `ridge’ on one side and a stream on the other. Further, on Axel-Heiberg, polygons that formed within the last 60 years are more orthogonal the closer they are to a lake. These observations suggest that polygon shape is controlled by soil moisture. It is commonly accepted that polygons form as a result of thermal contraction cracking followed by ice- or sand-wedge formation, and field studies suggest that the formation of ice-wedges over sand-wedges can be explained by elevated soil or air moisture. Sand-wedges potentially are more deformable than ice-wedges, allowing for the fracture network to evolve and relax into a hexagonal pattern, whereas ice-wedges would preserve the initial, orthogonal, pattern. Consequently, we hypothesise that the number and size of ice-wedges decreases along soil moisture gradients, and hence polygons farther away from water sources evolve into hexagonal shapes over repeated fracture cycles. This would mean that existing streams and other water sources set up gradients in the amount of ice stored throughout a polygon field, which in turn will influence both surface and groundwater flow during permafrost thaw, pointing towards complex interactions between polygons and landscape evolution in a changing climate.

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)0.002
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 score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.039
GPT teacher head0.314
Teacher spread0.276 · 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 routes2
Has abstractyes

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