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Record W2559899344 · doi:10.1175/jcli-d-16-0388.1

Sea Level Fingerprints in a Region of Complex Earth Structure: The Case of WAIS

2016· article· en· W2559899344 on OpenAlexaff
Carling C. Hay, H. C. P. Lau, Natalya Gomez, Jacqueline Austermann, Evelyn Powell, J. X. Mitrovica, Konstantin Latychev, Douglas A. Wiens

Bibliographic record

VenueJournal of Climate · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of TorontoMcGill University
FundersNational Science Foundation
KeywordsGeologySea levelLithosphereMagnitude (astronomy)GeophysicsClimatologySeismologyOceanographyPhysicsTectonics

Abstract

fetched live from OpenAlex

Abstract Sea level fingerprints associated with rapid melting of the West Antarctic Ice Sheet (WAIS) have generally been computed under the assumption of a purely elastic response of the solid Earth. The authors investigate the impact of viscous effects on these fingerprints by computing gravitationally self-consistent sea level changes that adopt a 3D viscoelastic Earth model in the Antarctic region consistent with available geological and geophysical constraints. In West Antarctica, the model is characterized by a thin (~65 km) elastic lithosphere and sublithospheric viscosities that span three orders of magnitude, reaching values as low as approximately 4 × 1018 Pa s beneath WAIS. Calculations indicate that sea level predictions in the near field of WAIS will depart significantly from elastic fingerprints in as little as a few decades. For example, when viscous effects are included, the peak sea level fall predicted in the vicinity of WAIS during a melt event will increase by about 20% and about 50%, relative to the elastic case, for events of duration 25 and 100 yr, respectively. The results have implications for studies of sea level change due to both ongoing mass loss from WAIS over the next century and future, large-scale collapse of WAIS on centennial-to-millennial time scales.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.958

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 studies0.0000.000
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 head0.066
GPT teacher head0.292
Teacher spread0.226 · 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

Citations75
Published2016
Admission routes1
Has abstractyes

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