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Record W2128380931 · doi:10.1002/grl.50820

The influence of recent Antarctic ice sheet retreat on simulated sea ice area trends

2013· article· en· W2128380931 on OpenAlexafffund
Neil C. Swart, John C. Fyfe

Bibliographic record

VenueGeophysical Research Letters · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaWestern Canada Research GridCompute CanadaUniversity of Victoria
KeywordsIce sheetForcing (mathematics)Coupled model intercomparison projectClimatologySea iceAntarctic ice sheetIce-sheet modelGeologyEnvironmental scienceAntarctic sea iceOceanographyClimate changeArctic ice packClimate modelCryosphere

Abstract

fetched live from OpenAlex

Observations indicate that dynamic mass loss from the Antarctic ice sheet has been accelerating over recent decades, leading to a freshening of the Southern Ocean. Here we quantify the effect of several rates and spatial distributions of freshwater forcing on simulated sea ice area trends. In contrast to a previous study, our simulations show that the freshwater effect on sea ice trends over the historical period is small and fails to reproduce the observed regional pattern of trends, when using observationally consistent rates of freshwater forcing. The Coupled Model Intercomparison Project Phase 5 (CMIP5) models do not represent this dynamic ice sheet mass loss, and it has been suggested that this deficiency may significantly influence the simulated sea ice trends. We show that when accounting for internal variability, the average CMIP5 sea ice area trend is statistically consistent with the observed trend, and accounting for ice sheet derived freshwater forcing has little influence.

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)0.004
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 score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
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 integrity0.0010.000
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 head0.048
GPT teacher head0.292
Teacher spread0.244 · 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

Citations142
Published2013
Admission routes2
Has abstractyes

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