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Record W4322005254 · doi:10.5194/egusphere-egu23-7408

Landscape generation by subglacial hydrology beneath the Fennoscandian Ice Sheet

2023· preprint· en· W4322005254 on OpenAlexaff
Adam Hepburn, Christine F. Dow, Antti Ojala, J. Mäkinen, Ahokangas Elina, Jukka‐Pekka Palmu, Jussi Hovikoski, Kari Kajuutti

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGeologyMeltwaterIce streamGeomorphologyLandformIce sheetGlacierDigital elevation modelSediment transportGlacier morphologySedimentHydrology (agriculture)CryosphereOceanographySea iceRemote sensing

Abstract

fetched live from OpenAlex

Unknown basal characteristics limit our ability to simulate the subglacial hydrology of rapidly thinning contemporary ice sheets. Sediment-based landforms deposited beneath former ice sheets can provide crucial information about basal hydrology during rapid ice loss. Murtoos—low-relief (5–10 m) features with a distinct triangular morphology—have been identified throughout Finland and Sweden within terrain formerly occupied by the Fennoscandian Ice Sheet (FIS). The depositional environment and formation of murtoos are not yet predicted by existing models of subglacial landforms. Excavations have revealed that, distally, murtoos are composed of alternating facies of heterogeneous diamicton, with strong fabrics interbedded with sorted gravelly and sandy sediment. Proximally, murtoos exhibit glaciofluvial deposits, such as current ripples, transitional cross-bedding, and antidunal sinusoidal laminations reflecting alternating lower and upper flow regimes. Additionally, regional mapping has revealed a spatial association of murtoos with other meltwater features and a characteristic presence no closer than 40–60 km from the FIS margin at ~12 ka. Collectively, these indicate that murtoo deposition is accompanied by rapid increases in meltwater discharge—potentially within a single melt season—and is associated with areas of low effective pressure and the spatial onset of channelised drainage systems.We used the Ice Sheet System Model (ISSM) implementation of the Glacier Drainage System (GlaDS) model to investigate murtoo genesis beneath the FIS. We parametrised GlaDS using digital elevation models (25 m/pixel) and estimations of ice surface elevation given by viscously relaxing initially parabolic ice profiles. Transient surface melt was introduced to a stable hydrological system over 10,000 days via moulins randomly distributed throughout the model domain. Moulin discharge rates were calculated using a positive degree day scheme forced by a depressed contemporary climate. Sensitivity testing was carried out for several poorly constrained parameters in GlaDS, as well as for the initial ice geometry and climatic inputs. We first applied GlaDS to a specific corridor of ice-flow within the relatively low-relief Finnish Lake District, where murtoos are densely concentrated, and then to a high-relief area of the Scandinavian Mountains towards which the FIS retreated prior to its demise. Murtoo density, as well as their gemorphic characteristics, was compared to the modelled sheet thickness, channel cross-sectional area, water pressure, and discharge rates through both the distributed and channelised system. Our modelling reproduces the hypothesised area of low effective pressure 40–60 km from the margin and supports the hypothesis that murtoos form in highly dynamic areas of the basal water system. This work highlights the value of applying GlaDS to glaciated regions in which hydrological outputs can be compared directly to geomorphological evidence.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.254

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.041
GPT teacher head0.232
Teacher spread0.191 · 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
Published2023
Admission routes1
Has abstractyes

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