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Record W4383221744 · doi:10.1126/sciadv.adf0198

Constraining the contribution of the Antarctic Ice Sheet to Last Interglacial sea level

2023· article· en· W4383221744 on OpenAlexaff
Robert L. Barnett, Jacqueline Austermann, Blake Dyer, Matt W. Telfer, Natasha Barlow, Sarah J. Boulton, Andrew S. Carr, Roger Creel

Bibliographic record

VenueScience Advances · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of VictoriaUniversité du Québec à Rimouski
FundersAkademie der NaturwissenschaftenEuropean CommissionUniversity of ExeterPast Global ChangesChinese Academy of SciencesNational Institutes of HealthNational Science Foundation
KeywordsInterglacialIce sheetFuture sea levelGreenland ice sheetGeologyAntarctic ice sheetIce coreIce-sheet modelCryosphereClimatologyOceanographyPhysical geographySea iceSea levelIce streamGlacial periodGeographyPaleontology

Abstract

fetched live from OpenAlex

Polar temperatures during the Last Interglacial [LIG; ~129 to 116 thousand years (ka)] were warmer than today, making this time period an important testing ground to better understand how ice sheets respond to warming. However, it remains debated how much and when the Antarctic and Greenland ice sheets changed during this period. Here, we present a combination of new and existing absolutely dated LIG sea-level observations from Britain, France, and Denmark. Because of glacial isostatic adjustment (GIA), the LIG Greenland ice melt contribution to sea-level change in this region is small, which allows us to constrain Antarctic ice change. We find that the Antarctic contribution to LIG global mean sea level peaked early in the interglacial (before 126 ka), with a maximum contribution of 5.7 m (50th percentile, 3.6 to 8.7 m central 68% probability) before declining. Our results support an asynchronous melt history over the LIG, with an early Antarctic contribution followed by later Greenland Ice Sheet mass loss.

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)0.001
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 score0.019
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.034
GPT teacher head0.301
Teacher spread0.268 · 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

Citations40
Published2023
Admission routes1
Has abstractyes

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