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Record W4285702303 · doi:10.22215/etd/2020-13905

Modelling ice island calving events with Finite Element Analysis

2020· dissertation· en· W4285702303 on OpenAlexafffund
Jesse Smith

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsCarleton University
FundersOntario Ministry of Research and InnovationArcticNet
KeywordsGeologyIcebergFracture (geology)Enhanced Data Rates for GSM EvolutionFinite element methodBuoyancyStress (linguistics)Sea iceIce calvingBendingGeomorphologyGeometryPaleontologyOceanographyStructural engineeringMechanicsEngineeringMathematicsPhysicsTelecommunications

Abstract

fetched live from OpenAlex

Ice islands, massive tabular icebergs, are known to fracture (calve) into fragments as they drift.One proposed calving mechanism occurs when a large protuberance, known as a ram, develops along the submerged edge of the ice island and induces a bending stress due to its buoyancy.To examine the relationship between rams and ice island fracture, polygons of ice islands derived from remote sensing imagery were used to create 3-D representations with synthesized rams.Associated stress and fractures were predicted using a Finite Element Analysis (FEA) and the results were compared to polygons of the actual fractured pieces.Modelled ice islands calve accurately when a synthesized ram is placed only along the edge that breaks off.An empirical model was developed to predict stress magnitude, which indicated the length of the ram, ram extent, and the ratio of ram volume to total ice volume play a central role in 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 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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.023
GPT teacher head0.223
Teacher spread0.201 · 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

Citations5
Published2020
Admission routes2
Has abstractyes

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