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Record W3168018483 · doi:10.5194/egusphere-egu21-13786

Reassessing the Contribution of West Antarctica to Last Interglacial Sea Level in Light of 3D Mantle Viscosity Structure

2021· article· en· W3168018483 on OpenAlexaff
Linda Pan, Evelyn Powell, Konstantin Latychev, J. X. Mitrovica, Jessica R. Creveling, Natalya Gomez, Mark Hoggard, Peter U. Clark

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsInterglacialAntarctic ice sheetGeologyMeltwaterIce sheetIce-sheet modelOceanographyGlacial periodMantle (geology)Sea levelClimatologyPost-glacial reboundSea icePaleontologyCryosphereIce stream

Abstract

fetched live from OpenAlex

Studies of peak global mean sea level (GMSL) during the Last Interglacial (LIG; 130-116 ka) commonly cite values ranging from ~2-5 m for the maximum contribution from grounded, marine-based sectors of the West Antarctic Ice Sheet (WAIS). However, this estimate neglects viscoelastic crustal uplift and the associated meltwater flux out of marine sectors as they are exposed, a contribution considered to be small and slowly-accumulating. This assumption should be revisited, as a range of evidence indicates that West Antarctica is underlain by shallow mantle of anomalously low viscosity. By incorporating this complex structure into a gravitationally self-consistent sea-level calculation, we find that GMSL differs substantially from previous estimates. Our results indicate that these estimates thus require a reassessment of the contribution to GMSL rise from WAIS collapse, as will ice sheet models that do not account for the uplift mechanism. This conclusion has important implications for the sea level budget not only during the LIG, but also for all previous interglacials and projections of GMSL change in the future warming world.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.019
GPT teacher head0.267
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2021
Admission routes1
Has abstractyes

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