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Record W4385810083 · doi:10.1017/jog.2023.58

Ocean wave blocking by periodic surface rolls fortifies Arctic ice shelves

2023· article· en· W4385810083 on OpenAlexaboutno aff
Peter Nekrasov, Douglas R. MacAyeal

Bibliographic record

VenueJournal of Glaciology · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
FundersUniversity of ChicagoNational Science Foundation
KeywordsGeologyIce shelfSea iceArctic ice packAntarctic sea iceIce divideFront (military)ArcticOceanographyDrift iceIce calvingClimatologyCryosphere

Abstract

fetched live from OpenAlex

Abstract The Ward Hunt and Milne ice shelves are the present-day remnants of a much larger ice shelf that once fringed the coast of Ellesmere Island, Canada. These ice shelves possess a unique surface morphology consisting of wave-like rolls that run parallel to the shoreline. Setting aside the question of how these rolls originally developed, we consider the impact of this roll morphology on the stability of the ice shelf. In particular, we examine whether periodic variations in ice-shelf thickness and water depth implied by the rolls prevent the excitation of Lamb waves in the ice shelf. Using a hierarchy of numerical models, we find that there are band gaps in the flexural and extensional modes of the ice shelf, implying the existence of frequency ranges that lack wave motion. We show that an ice shelf with rolls is able to reflect waves in these frequency ranges that are incident upon its ice front, thereby mitigating undue stress and calving. We speculate that the roll morphology provides a “fitness” for survival that explains why rolls are observed in the oldest and thickest multiyear sea ice of the Arctic.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
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 head0.029
GPT teacher head0.234
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations6
Published2023
Admission routes1
Has abstractyes

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