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Record W4411550410 · doi:10.1139/as-2023-0069

Long-term field tracking of icebergs in the eastern Canadian Arctic

2025· article· en· W4411550410 on OpenAlexaffvenueabout
Abigail Dalton, Adam Garbo, Luke Copland, Wesley Van Wychen, Derek Mueller, Adrienne Tivy, Juliana M. Marson

Bibliographic record

VenueArctic Science · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of ManitobaEnvironment and Climate Change CanadaCarleton UniversityUniversity of WaterlooUniversity of Ottawa
Fundersnot available
KeywordsIcebergTerm (time)ArcticField (mathematics)Tracking (education)GeographyThe arcticOceanographyEnvironmental scienceGeologyMeteorologySea icePhysicsMathematicsAstronomyPsychology

Abstract

fetched live from OpenAlex

Tidewater glaciers are those which terminate into the ocean and drain a significant proportion of the Greenland Ice Sheet and ice masses of the Canadian Arctic, providing the primary source of icebergs in Canadian waters. Once calved, there remains uncertainty concerning the processes controlling their drift. This study uses a multi-year dataset (2011–2019) of in situ iceberg observations to characterize drift on a regional scale throughout Baffin Bay. We identify common grounding areas and quantify the influence of wind, ocean, and tidal currents using ERA5 climate reanalysis, global ocean reanalysis and simulations, and WebTide Tidal Prediction models. Icebergs in the Eastern Canadian Arctic consistently drifted southeast along the east coast of Baffin Island. We evaluate the assumption that icebergs drift at 2% of the wind speed and determine that this rule does not apply for the majority of icebergs in this study, which often exceeded 2% of the wind speed, in particular at low wind speeds. The highest speeds occurred during the winter and spring, reaching up to 2.3 m s −1 in Nares Strait. Our analysis indicates that iceberg drift patterns are controlled by a combination of local conditions including short-term wind events, ocean surface currents, and semi-diurnal tidal oscillations.

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 score0.331
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.041
GPT teacher head0.275
Teacher spread0.234 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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