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Record W3112788654 · doi:10.1017/jog.2020.99

Drygalski Ice Tongue stability influenced by rift formation and ice morphology

2020· article· en· W3112788654 on OpenAlexafffund
Christine Indrigo, Christine F. Dow, Jamin S. Greenbaum, Mathieu Morlighem

Bibliographic record

VenueJournal of Glaciology · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaKorea Polar Research InstituteNatural Environment Research CouncilMinistry of Oceans and FisheriesNational Aeronautics and Space AdministrationCanada Research ChairsKorea Institute of Marine Science and Technology promotionNational Science FoundationUniversity of CambridgeG. Unger Vetlesen Foundation
KeywordsGeologyIce tongueIce calvingIce streamIce shelfGeomorphologyAntarctic sea iceFast iceSea iceIce divideArctic ice packCryosphereOceanographyPaleontology

Abstract

fetched live from OpenAlex

Abstract The Drygalski Ice Tongue in East Antarctica stretches 90 km into the Ross Sea and influences the local ocean circulation, and persistence of the Terra Nova Bay Polynya. We examine the controls on the size of this floating ice body by comparing the propagation of six large fractures on the ice tongue's northern side using 21 years of Landsat imagery with hydrostatic ice thickness maps and strain rate calculations. We also apply a subglacial hydrology model to estimate the location and discharge from subglacial channels over the grounding line and compare these with basal channels identified along the ice tongue using remote sensing and airborne radar data. Our results suggest that large fractures are inhibited from full-width propagation by thicker ice between basal channels. We hypothesize that only once the ice tongue thins towards the terminus, can fractures propagate and cause large calving events. This suggests an important relationship between the melting of floating ice from subglacial and ocean sources and the expansion of fractures that lead to ice tongue calving.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.723

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)0.0010.000

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 head0.026
GPT teacher head0.229
Teacher spread0.203 · 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

Citations33
Published2020
Admission routes2
Has abstractyes

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