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Record W3206115997 · doi:10.1130/g49388.1

Lateral variation in slab window viscosity inferred from global navigation satellite system (GNSS)–observed uplift due to recent mass loss at Patagonia ice fields

2021· article· en· W3206115997 on OpenAlexaff
R. M. Russo, Haipeng Luo, Kelin Wang, Boudewijn Ambrosius, V. Mocanu, Jiangheng He, T. S. James, Michael Bevis, Rui Fernandes

Bibliographic record

VenueGeology · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsGeological Survey of CanadaUniversity of Victoria
FundersUniversidad de ChileTechnische Universiteit DelftGeological Society of America
KeywordsGeologyPost-glacial reboundSlabSlab windowGeophysicsSubductionIce fieldLithosphereMantle (geology)RidgeGeodesySeismologyGlacial periodGeomorphologyTectonicsOceanic crustPaleontologyGlacier

Abstract

fetched live from OpenAlex

Abstract The geographic coincidence of the Chile Ridge slab window and the Patagonia ice fields offers a unique opportunity for assessing the effects of slab window rheology on glacial isostatic adjustment (GIA). Mass loss of these ice fields since the Little Ice Age causes rapid but variable crustal uplift, 12–24 mm/yr around the North Patagonia ice field, increasing to a maximum of 41 mm/yr around the South Patagonia ice field, as determined from newly collected or processed geodetic data. We used these observational constraints in a three-dimensional Maxwell viscoelastic finite element model of GIA response above both the subducting slab and slab window in which the upper-mantle viscosity was parameterized to be uniform with depth. We found that the viscosity of the northern part of the slab window, ~2 × 1018 Pa·s, is lower than that of the southern part by approximately an order of magnitude. We propose that this along-strike viscosity contrast is due to late Cenozoic ridge subduction beneath the northern part of the slab window, which increases asthenospheric temperature and reduces viscosity.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score1.000

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)0.0010.000

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 head0.018
GPT teacher head0.219
Teacher spread0.201 · 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 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

Citations22
Published2021
Admission routes1
Has abstractyes

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