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Record W2058178494 · doi:10.5194/tc-7-1185-2013

Influence of ice-sheet geometry and supraglacial lakes on seasonal ice-flow variability

2013· article· en· W2058178494 on OpenAlexafffund
Ian Joughin, Sarah B. Das, Gwenn E. Flowers, M. D. Behn, Richard B. Alley, Matt A. King, B. E. Smith, Jonathan Bamber, M. R. van den Broeke, J. H. van Angelen

Bibliographic record

Venue˜The œcryosphere · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsSimon Fraser University
FundersOffice of Polar ProgramsNatural Environment Research CouncilNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsMeltwaterGeologyIce streamIce sheetFlow (mathematics)GeomorphologyGreenland ice sheetCryosphereHydrology (agriculture)ClimatologyGlacierSea iceGeometry

Abstract

fetched live from OpenAlex

Abstract. Supraglacial lakes play an important role in establishing hydrological connections that allow lubricating seasonal meltwater to reach the base of the Greenland Ice Sheet. Here we use new surface velocity observations to examine the influence of supraglacial lake drainages and surface melt rate on ice flow. We find large, spatially extensive speedups concurrent with times of lake drainage, showing that lakes play a key role in modulating regional ice flow. While surface meltwater is supplied to the bed via a geographically sparse network of moulins, the observed ice-flow enhancement suggests that this meltwater spreads widely over the ice-sheet bed. We also find that the complex spatial pattern of speedup is strongly determined by the combined influence of bed and surface topography on subglacial water flow. Thus, modeling of ice-sheet basal hydrology likely will require knowledge of bed topography resolved at scales (sub-kilometer) far finer than existing data (several km).

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 score0.032
Threshold uncertainty score0.995

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)0.0060.000

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 head0.010
GPT teacher head0.202
Teacher spread0.192 · 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

Citations97
Published2013
Admission routes2
Has abstractyes

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