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Record W3158448730 · doi:10.1126/sciadv.abf7787

Rapid postglacial rebound amplifies global sea level rise following West Antarctic Ice Sheet collapse

2021· article· en· W3158448730 on OpenAlexafffund
Linda Pan, Evelyn Powell, Konstantin Latychev, J. X. Mitrovica, Jessica R. Creveling, Natalya Gomez, Mark Hoggard, Peter U. Clark

Bibliographic record

VenueScience Advances · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaAmerican Chemical Society Petroleum Research FundOregon State UniversityCanada Research ChairsNational Aeronautics and Space AdministrationHarvard UniversityJohn D. and Catherine T. MacArthur FoundationNational Science Foundation
KeywordsAntarctic ice sheetIce sheetGeologySea level riseFuture sea levelClimatologyOceanographyPhysical geographyCryosphereIce streamClimate changeSea iceGeography

Abstract

fetched live from OpenAlex

Geodetic, seismic, and geological evidence indicates that West Antarctica is underlain by low-viscosity shallow mantle. Thus, as marine-based sectors of the West Antarctic Ice Sheet (WAIS) retreated during past interglacials, or will retreat in the future, exposed bedrock will rebound rapidly and flux meltwater out into the open ocean. Previous studies have suggested that this contribution to global mean sea level (GMSL) rise is small and occurs slowly. We challenge this notion using sea level predictions that incorporate both the outflux mechanism and complex three-dimensional viscoelastic mantle structure. In the case of the last interglacial, where the GMSL contribution from WAIS collapse is often cited as ~3 to 4 meters, the outflux mechanism contributes ~1 meter of additional GMSL change within ~1 thousand years of the collapse. Using a projection of future WAIS collapse, we also demonstrate that the outflux can substantially amplify GMSL rise estimates over the next century.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0000.001
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 head0.272
Teacher spread0.240 · 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

Citations76
Published2021
Admission routes2
Has abstractyes

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