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Record W4409435338 · doi:10.1017/jog.2025.36

The effect of melt-channel geometry on ice-shelf flow

2025· article· en· W4409435338 on OpenAlexaff
David A. Lilien, Karen E. Alley

Bibliographic record

VenueJournal of Glaciology · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Manitoba
FundersNuclear Safety and Security CommissionLilly EndowmentEli Lilly and CompanyNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsGeologyChannel (broadcasting)Ice shelfGeometryFlow (mathematics)GeomorphologyOceanographySea iceCryosphereMathematics

Abstract

fetched live from OpenAlex

Abstract Basal channels are incised troughs formed by elevated melt beneath ice shelves. Channels often coincide with shear margins, suggesting feedbacks between channel formation and shear. However, the effect of channel position and shape on ice-shelf flow has not been systematically explored. We use a model to show that, as expected, channels concentrate deformation and increase ice-shelf flow speeds, in some cases by over 100% at the ice-shelf center and over 80% at the grounding line. The resulting increase in shear can cause stresses around the channels to exceed the threshold for failure, suggesting that rifting, calving and retreat might result. However, channels have different effects depending on their width, depth and position on an ice shelf. Channels in areas where ice shelves are spreading freely have little effect on ice flow, and even channels in confined regions of the shelf do not necessarily alter flow significantly. Nevertheless, if located in areas of vulnerability, particularly in the shear margins near the grounding line, melt channels may alter flow in a way that could lead to catastrophic ice-shelf breakup by mechanically separating shelves from their embayments.

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 score0.128
Threshold uncertainty score0.159

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 head0.009
GPT teacher head0.236
Teacher spread0.227 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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