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Record W2284585119 · doi:10.1002/2016gl067720

The implication of nonradiative energy fluxes dominating Greenland ice sheet exceptional ablation area surface melt in 2012

2016· article· en· W2284585119 on OpenAlexaff
Robert S. Fausto, D. van As, Jason E. Box, William Colgan, Peter L. Langen, Ruth Mottram

Bibliographic record

VenueGeophysical Research Letters · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsYork University
FundersNationale Geologiske Undersøgelser for Danmark og GrønlandDanmarks Frie ForskningsfondNordForsk
KeywordsGreenland ice sheetClimatologyEnvironmental scienceIce sheetAblationAblation zoneAtmospheric sciencesClimate modelDominance (genetics)GeologyClimate changeOceanographyGlacierGeomorphology

Abstract

fetched live from OpenAlex

Abstract During two exceptionally large July 2012 multiday Greenland ice sheet melt episodes, nonradiative energy fluxes (sensible, latent, rain, and subsurface collectively) dominated the ablation area surface energy budget of the southern and western ice sheet. On average the nonradiative energy fluxes contributed up to 76% of daily melt energy at nine automatic weather station sites in Greenland. Comprising 6% of the ablation period, these powerful melt episodes resulted in 12–15% of the south and west Greenland automatic weather station annual ablation totals. Analysis of high resolution (~5 km) HIRHAM5 regional climate model output indicates widespread dominance of nonradiative energy fluxes across the western ablation area during these episodes. Yet HIRHAM5 still underestimates melt by up to 56% during these episodes due to a systematic underestimation of turbulent energy fluxes typical of regional climate models. This has implications for underestimating future melt, when exceptional melt episodes are expected to occur more frequently.

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)0.000
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 score0.449
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.037
GPT teacher head0.282
Teacher spread0.246 · 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

Citations108
Published2016
Admission routes1
Has abstractyes

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