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Record W4416980558 · doi:10.5194/gmd-18-9565-2025

The glacial systems model (GSM) Version 25G

2025· article· en· W4416980558 on OpenAlexafffund
Lev Tarasov, Benoit S. Lecavalier, Kevin Hank, David F. Pollard

Bibliographic record

VenueGeoscientific model development · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsDefence Research and Development CanadaMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIce-sheet modelGlacial periodIce sheetSea iceContext (archaeology)PermafrostPost-glacial reboundLast Glacial Maximum

Abstract

fetched live from OpenAlex

Abstract. We document the glacial system model (GSM), which is designed for large ensemble ice sheet modelling in glacial cycle contexts. A distinguishing feature is the extent to which it addresses relevant forcing and process uncertainties. The GSM has evolved from three decades of effort to constrain the last glacial cycle evolution of each ice sheet that was present (North American, Greenlandic, Icelandic, Eurasian, Patagonian, and Antarctic, and soon Tibetan). The core ice dynamics uses a hybrid shallow-shelf and shallow-ice approximation with full thermo-mechanical coupling. It also includes one of the largest range of relevant processes for the above context of any model to date, ranging from visco-elastic glacial isostatic adjustment with 0-order geoidal deflection to state-of-the-art subglacial sediment production, transport, and deposition. Furthermore, the GSM is to date the only model to have all of the above processes bidirectionally coupled with each other. Other relevant distinguishing features include: permafrost resolving bed-thermodynamics, a fast diagnostic solution of down-slope surface drainage and lake filling, subgrid hypsometric surface mass balance and ice flow, simple thermodynamic lake and sea ice representations, subglacial hydrology with dynamically evolving partitioning between distributed and channelized flow, and surface melt that physically accounts for insolation changes via a novel insolation above freezing scheme. To address the most challenging part of paleo ice sheet modelling, the GSM includes both a 2D energy balance climate model and variants of traditional input time series weighted interpolation (aka “glacial indexing”) of fields from General Circulation Model (GCM) simulations, all under ensemble parametric specification. It also includes options for one and two way scripted coupling with climate models. We demonstrate the significant errors that can ensue in the glacial cycle simulation of a single ice sheet when three aspects of glacial isostatic adjustment are ignored (as is typical). These are geoidal deformation, global ice load input, and correction of initial topography for present-day isostatic disequilibrium. We also draw attention to the relatively high sensitivity of the GSM (and presumably other ice sheet models) to the specification of the temperature dependence for basal sliding activation. The associated code archive includes configuration options for all major last glacial cycle ice sheets as well as idealized geometries and validation test setups.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.211
Teacher spread0.190 · 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 teacher head, not a consensus.

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

Citations2
Published2025
Admission routes2
Has abstractyes

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