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Record W4387671183 · doi:10.5194/egusphere-2023-2141

Reorganisation of subglacial drainage processes during rapid melting of the Fennoscandian Ice Sheet

2023· preprint· en· W4387671183 on OpenAlexafffund
Adam Hepburn, Christine F. Dow, Antti Ojala, Joni Mäkinen, Elina Ahokangas, Jussi Hovikoski, Jukka‐Pekka Palmu, Kari Kajuutti

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Waterloo
FundersAlliance de recherche numérique du CanadaEuropean CommissionCanada Excellence Research Chairs, Government of CanadaAberystwyth UniversityTurun YliopistoAcademy of FinlandEuropean Space Agency
KeywordsLandformGeologyDrainageGeomorphologyDigital elevation modelIce sheetDrainage system (geomorphology)GlacierElevation (ballistics)Hydrology (agriculture)Geotechnical engineeringRemote sensingGeometryBiology

Abstract

fetched live from OpenAlex

Abstract. Unknown basal characteristics limit our ability to simulate the subglacial hydrology of rapidly thinning contemporary ice sheets. Subglacial water is typically conceptualised as being routed through either distributed, inefficient, and high pressure systems, or channelised, efficient, and low-pressure systems, transitioning between the two as a function of discharge. Sediment-based landforms generated beneath Pleistocene ice sheets, together with detailed digital elevation models, offer a valuable means of parameterising and testing models of subglacial hydrology. However, previous work using geomorphology to inform modelling has concentrated on landforms relating to channelised drainage (e.g., eskers) while using inherently channelised models unable to capture transitions in drainage state. Landscapes relating to the distributed drainage system, and the hypothesised transitional zone of drainage between distributed and channelised drainage modes have therefore been largely ignored. To address this, we use the Glacier Drainage System model (GlaDS), a 2D finite element model capable of capturing the transition between distributed and channelised drainage, to explore the genesis of ‘murtoos’, a distinctive triangular landform found in murtoo fields throughout Finland and Sweden. Murtoos are hypothesised to form 40–60 km from the former Fennoscandian ice margin at the onset of channelised drainage where water pressure is at or exceeds ice overburden pressure. Concentrating within a specific ice lobe of the former Fennoscandian Ice Sheet and parameterised using digital elevation models with a simulated former ice surface geometry, we carried out a range of sensitivity testing to explore murtoo genesis and drainage transitions beneath the palaeo ice sheet. Our modelling supports many of the predictions for murtoo origin, including the location of water pressures equal to ice overburden, the onset of channelised drainage, and the predicted water depths in terrain surrounding murtoo fields. Modelled channels also closely match the general spacing, direction and complexity of eskers and mapped meltwater routes. Our results demonstrate that examining palaeo basal topography alongside subglacial hydrology model outputs holds promise for mutually beneficial analyses of palaeo and contemporary ice sheets to assess the controls of hydrology on ice dynamics and subglacial landform evolution.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.152

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 studies0.0000.001
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 head0.032
GPT teacher head0.223
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 designSimulation or modeling
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

Citations1
Published2023
Admission routes2
Has abstractyes

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