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Record W2907361106 · doi:10.1038/s41467-018-08068-y

Solid Earth change and the evolution of the Antarctic Ice Sheet

2019· review· en· W2907361106 on OpenAlexafffund
Pippa L. Whitehouse, Natalya Gomez, Matt A. King, Douglas A. Wiens

Bibliographic record

VenueNature Communications · 2019
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsMcGill University
FundersMcGill UniversityNatural Environment Research CouncilAustralian Research CouncilSight Research UKNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsIce sheetIce-sheet modelGeologyIce divideIce streamAntarctic ice sheetFuture sea levelAntarctic sea iceCryosphereLead (geology)Ice-albedo feedbackClimate changeSea iceAstrobiologyOceanographyGeomorphologyPhysics

Abstract

fetched live from OpenAlex

Recent studies suggest that Antarctica has the potential to contribute up to ~15 m of sea-level rise over the next few centuries. The evolution of the Antarctic Ice Sheet is driven by a combination of climate forcing and non-climatic feedbacks. In this review we focus on feedbacks between the Antarctic Ice Sheet and the solid Earth, and the role of these feedbacks in shaping the response of the ice sheet to past and future climate changes. The growth and decay of the Antarctic Ice Sheet reshapes the solid Earth via isostasy and erosion. In turn, the shape of the bed exerts a fundamental control on ice dynamics as well as the position of the grounding line-the location where ice starts to float. A complicating issue is the fact that Antarctica is situated on a region of the Earth that displays large spatial variations in rheological properties. These properties affect the timescale and strength of feedbacks between ice-sheet change and solid Earth deformation, and hence must be accounted for when considering the future evolution of the ice sheet.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.086
GPT teacher head0.322
Teacher spread0.236 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations187
Published2019
Admission routes2
Has abstractyes

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