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Record W4396643382 · doi:10.1029/2023jc020697

Sea Ice‐Driven Iceberg Drift in Baffin Bay

2024· article· en· W4396643382 on OpenAlexafffundabout
Juliana M. Marson, Paul G. Myers, Adam Garbo, Luke Copland, Derek Mueller

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsCarleton UniversityUniversity of OttawaUniversity of AlbertaUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of AlbertaPolar Knowledge Canada
KeywordsIcebergBayOceanographySea iceGeologyClimatology

Abstract

fetched live from OpenAlex

Abstract Baffin Bay is the travel destination of most icebergs calving from west Greenland. They commonly follow the bay's cyclonic circulation and might end up far south along the coast of Newfoundland and Labrador, where many shipping routes converge. Given the hazard that icebergs pose to marine transportation, understanding their distribution is fundamental. One of the forces driving iceberg drift arises from the presence of sea ice. Observations in the Southern Ocean indicate that icebergs get locked in thick and concentrated sea ice. We present observations that support the occurrence of this sea ice locking mechanism (SIL) in Baffin Bay as well. Most iceberg models, however, represent the sea ice force over an iceberg as a simple drag force. Here, we implement a new parameterization in the iceberg module of the Nucleus for European Modeling of the Ocean (NEMO‐ICB) to represent SIL. We show that, by using this new parameterization, icebergs are more likely to travel outside of the Baffin Island Current during winter, which is supported by satellite observations. There is a slight improvement in the representation of iceberg severity along the coast of Newfoundland and Labrador and a slight shift of iceberg melt toward this region and Lancaster Sound/Hudson Strait. Although the impacts of icebergs on sea ice are still not represented, and targeted observations are needed for model calibration regarding sea ice concentration thresholds from which icebergs get locked, we are confident that this model improvement takes iceberg modeling one step forward toward reality.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.766

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.027
GPT teacher head0.306
Teacher spread0.280 · 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 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

Citations5
Published2024
Admission routes3
Has abstractyes

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