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Record W156563788

Sea level curves, geoid rate and uplift rate from composite rheology in glacial isostatic adjustment modeling

2009· article· en· W156563788 on OpenAlexaff
Wouter van der Wal, Patrick Wu, H. Wang, Michael G. Sideris

Bibliographic record

VenueEGUGA · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRheologyPost-glacial reboundCreepGeologyGeoidViscosityMantle (geology)MechanicsGeophysicsThermodynamicsGlacial periodPhysicsGeomorphology
DOInot available

Abstract

fetched live from OpenAlex

Laboratory experiments show that both diffusion creep and power-law creep can exist for realistic mantle conditions. Therefore a composite rheology which includes both creep laws might be a better approximation of the deformation process in the mantle. Here we study the effect of such a rheology on glacial isostatic adjustment (GIA) observables. Composite rheology has in the past been shown to provide a better fit to sea level data for a wide range of parameters investigated with 2D finite element models. Here, we use the Coupled Laplace Finite Element Method for an incompressible 3D spherical self-gravitating Earth to study the effect of composite rheology on relative sea level (RSL) curves, maximum present-day uplift rate and maximum present-day geoid rate with the ICE-5G model. The long computation time of this model limits the number of cases that can be investigated to a handful. The stress exponent is taken to be 3, the pre-stress exponent (A) derived from a uni-axial stress experiment is varied between 3.3 x 10−33/10−34/10−35/10−36 Pa−3s−1, and the Newtonian viscosity η is varied between 1/3/9 x 10 Pas. Because the ice models are developed under the assumption of linear rheology any rheology with a non-linear component usually under predicts the uplift rate and geoid rate. Therefore, to see if the ice model can provide a better fit, we investigate simple modifications to the ICE-4G model, such as i) scaling of the ice height by 1.5 and 2.0; and ii) delay in glaciation by 1 and 2 kyears.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.107
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.037
GPT teacher head0.235
Teacher spread0.198 · 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.

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
Published2009
Admission routes1
Has abstractyes

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