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Record W2027950413 · doi:10.1175/2009jcli3122.1

Surface Melting over Ice Shelves and Ice Sheets as Assessed from Modeled Surface Air Temperatures

2009· article· en· W2027950413 on OpenAlexaff
Jeremy Fyke, Lionel Carter, Andrew Mackintosh, Andrew J. Weaver, Katrin J. Meißner

Bibliographic record

VenueJournal of Climate · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Victoria
FundersFoundation for Research, Science and Technology
KeywordsIce shelfIce sheetGeologyAntarctic sea iceCryosphereClimatologySea iceAntarctic ice sheetIce streamIce divideArctic ice packOceanography

Abstract

fetched live from OpenAlex

Abstract Summer surface melting plays an important role in the evolution of ice shelves and their progenitor ice sheets. To explore the magnitude of surface melt occurring over modern ice shelves and ice sheets in a climate scenario forced by anthropogenic emissions of carbon dioxide (CO2), a coupled climate model was used to simulate the distribution of summer melt at high latitudes and project the future evolution of high-melt regions in both hemispheres. Forcing of the climate model with CO2 emissions resulting from combustion of the present-day fossil-fuel resource base resulted in expansion of high-melt regions, as defined by the contour marking 200 positive degree-days per year, in the Northern Hemisphere and the Antarctic Peninsula and the introduction of high summer melt over the Ross, Ronne-Filchner, and Amery ice shelves as well as a large portion of the West Antarctic Ice Sheet (WAIS) and most of the Greenland Ice Sheet (GIS) by the year 2500. Capping CO2 concentrations at present-day levels avoided significant summer melt over the large Antarctic shelves, the WAIS, and much of the GIS.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.016
GPT teacher head0.255
Teacher spread0.239 · 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

Citations13
Published2009
Admission routes1
Has abstractyes

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