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Record W2808022527 · doi:10.1126/sciadv.aao7212

Basal channels drive active surface hydrology and transverse ice shelf fracture

2018· article· en· W2808022527 on OpenAlexafffund
Christine F. Dow, Won Sang Lee, Jamin S. Greenbaum, Chad A. Greene, Donald D. Blankenship, Kristin Poinar, Alexander L. Forrest, D. A. Young, Christopher J. Zappa

Bibliographic record

VenueScience Advances · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Waterloo
FundersMinistry of Oceans and FisheriesNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsNational Science Foundation
KeywordsIce shelfGeologyIce streamOceanographySea iceThinningGeomorphologyIcebergCryosphereGeography

Abstract

fetched live from OpenAlex

Ice shelves control sea-level rise through frictional resistance, which slows the seaward flow of grounded glacial ice. Evidence from around Antarctica indicates that ice shelves are thinning and weakening, primarily driven by warm ocean water entering into the shelf cavities. We have identified a mechanism for ice shelf destabilization where basal channels underneath the shelves cause ice thinning that drives fracture perpendicular to flow. These channels also result in ice surface deformation, which diverts supraglacial rivers into the transverse fractures. We report direct evidence that a major 2016 calving event at Nansen Ice Shelf in the Ross Sea was the result of fracture driven by such channelized thinning and demonstrate that similar basal channel-driven transverse fractures occur elsewhere in Greenland and Antarctica. In the event of increased basal and surface melt resulting from rising ocean and air temperatures, ice shelves will become increasingly vulnerable to these tandem effects of basal channel destabilization.

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 score0.316
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.242
Teacher spread0.231 · 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

Citations101
Published2018
Admission routes2
Has abstractyes

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