MétaCan
Menu
Back to cohort
Record W2901490327 · doi:10.1029/2018gl080763

Localized Plumes Drive Front‐Wide Ocean Melting of A Greenlandic Tidewater Glacier

2018· article· en· W2901490327 on OpenAlexaff
Donald Slater, Fiammetta Straneo, Sarah B. Das, Clark Richards, Till J. W. Wagner, P. W. Nienow

Bibliographic record

VenueGeophysical Research Letters · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsBedford Institute of Oceanography
FundersOcean Life Institute, Woods Hole Oceanographic InstitutionNatural Environment Research CouncilSight Research UKWoods Hole Oceanographic InstitutionNational Science Foundation
KeywordsTidewaterFjordGlacierGeologyTidewater glacier cycleGreenland ice sheetIce calvingFront (military)OceanographyFuture sea levelOcean currentClimatologyGeomorphologyIce streamCryosphereSea ice

Abstract

fetched live from OpenAlex

Abstract Recent acceleration of Greenland's ocean‐terminating glaciers has substantially amplified the ice sheet's contribution to global sea level. Increased oceanic melting of these tidewater glaciers is widely cited as the likely trigger, and is thought to be highest within vigorous plumes driven by freshwater drainage from beneath glaciers. Yet melting of the larger part of calving fronts outside of plumes remains largely unstudied. Here we combine ocean observations collected within 100 m of a tidewater glacier with a numerical model to show that unlike previously assumed, plumes drive an energetic fjord‐wide circulation which enhances melting along the entire calving front. Compared to estimates of melting within plumes alone, this fjord‐wide circulation effectively doubles the glacier‐wide melt rate, and through shaping the calving front has a potential dynamic impact on calving. Our results suggest that melting driven by fjord‐scale circulation should be considered in process‐based projections of Greenland's sea level contribution.

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 categoriesInsufficient payload (model declined to judge)
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 score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.036
GPT teacher head0.284
Teacher spread0.248 · 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; both teacher heads agree on what is shown here.

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

Citations125
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueGeophysical Research LettersSame topicCryospheric studies and observationsFrench-language works237,207