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Record W2901324547 · doi:10.1017/jfm.2018.798

Nonlinear influence of the Earth’s rotation on iceberg melting

2018· article· en· W2901324547 on OpenAlexaff
Agostino N. Meroni, Craig McConnochie, Claudia Cenedese, Bruce Sutherland, Kate Snow

Bibliographic record

VenueJournal of Fluid Mechanics · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Alberta
FundersWoods Hole Oceanographic InstitutionNational Science Foundation
KeywordsIcebergGeologyAdvectionMechanicsEarth's rotationGeodesyPhysicsThermodynamicsIce sheetGeomorphology

Abstract

fetched live from OpenAlex

The calving of icebergs accounts for a significant fraction of the mass loss from both the Antarctic and Greenland ice sheets. Iceberg melting affects the water properties impacting sea ice formation, local circulation and biological activity. Laboratory experiments have investigated the effects of the Earth’s rotation on iceberg melting and the possible formation of Taylor columns (TCs) underneath icebergs. It is found that at high Rossby number, $Ro$ , when rotation is weak compared to advection, iceberg melting is unaffected by the background rotation. As $Ro$ decreases, the melt rate shows an increasing trend, which is expected to reverse for very low $Ro$ . This behaviour is explained by considering the integrated horizontal velocity at the base of the iceberg. For moderate $Ro$ , a partial TC is formed and its effective blocking accelerates the flow under the remainder of the iceberg, which increases the melt rate since the melting is proportional to the flow velocity. It is expected that for very low $Ro$ the melt rate decreases because, with the expansion of the TC, the region of flow acceleration occurs away from the base of the iceberg. For low free stream velocity the freshwater produced by the ice melting introduces another dynamical effect. It is observed that there is a threshold free stream velocity below which the melt rate is constant. This is explained with the formation of a gravity current at the base of the iceberg that insulates it from the free flow and controls its melting.

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 score0.004
Threshold uncertainty score0.007

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.228
Teacher spread0.210 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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