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Record W3178025460 · doi:10.1029/2021ef002152

Possibility of Stabilizing the Greenland Ice Sheet

2021· article· en· W3178025460 on OpenAlexaff
Xiuquan Wang, Adam Fenech, Aitazaz A. Farooque

Bibliographic record

VenueEarth s Future · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Prince Edward Island
FundersNational Key Research and Development Program of China
KeywordsGreenland ice sheetIce sheetFuture sea levelIce-sheet modelClimatologyEnvironmental scienceGlobal warmingIce-albedo feedbackSea iceCryosphereClimate changeGeologyOceanographyIce stream

Abstract

fetched live from OpenAlex

Abstract Recent acceleration in the retreat of the Greenland ice sheet under a warming climate has caused unprecedented challenges and threats to coastal communities due to the rising sea level and increasing storm surges. This raises a critical question from a climate mitigation perspective: Would there still be a chance to stabilize the Greenland ice sheet if the carbon reduction goals of the Paris Agreement could be met? Here, we show that there is indeed a possibility for stabilizing the Greenland ice sheet with the low‐emission scenario of RCP2.6. In particular, RCP2.6 would potentially limit the warming in Greenland below 1°C within next 30 years and constrain its loss of ice sheet coverage below 10%. After 2050, the annual mean temperature in Greenland is likely to be stabilized and no further loss is expected to its ice sheet. However, the effective window for this chance will be closing after 2020. If no effective carbon reduction policies are being taken now, we are very likely to enter a continuous warming pathway and lose the chance of stabilizing the Greenland 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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0040.000

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 head0.016
GPT teacher head0.213
Teacher spread0.197 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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