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Record W2791584082 · doi:10.1002/2017gl076587

Increasing Mobility of High Arctic Sea Ice Increases Marine Hazards Off the East Coast of Newfoundland

2018· article· en· W2791584082 on OpenAlexafffundabout
David G. Barber, David G. Babb, Jens K. Ehn, Wayne Chan, Lisa Matthes, Laura A. Dalman, Yanique Campbell, Madison L. Harasyn, Nariman Firoozy, Nathalie Thériault, Jennifer V. Lukovich, Tom Zagon, Tim Papakyriakou, David Capelle, Alexandre Forest, Alain Gariepy

Bibliographic record

VenueGeophysical Research Letters · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversité LavalCanadian Armed ForcesUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsArcticNetManitoba Hydro
KeywordsSea iceOceanographyArctic ice packCoast guardArchipelagoArcticDrift iceGeologyCryosphereAntarctic sea iceArctic sea ice declineIcebergEnvironmental scienceEnvironmental protection

Abstract

fetched live from OpenAlex

Abstract Heavy ice conditions along Canada's east coast during spring 2017 presented hazardous conditions for the maritime industry and required the Canadian Coast Guard to pull its research icebreaker, CCGS Amundsen , off its scientific cruise to provide ice escort services and conduct search and rescue operations along Newfoundland's northeast coast. Greater ice concentrations and a thicker ice pack than are typical of this area created the anomalous ice cover. Within this paper we present in situ observations of the ice cover, confirming that pieces of multiyear sea ice from the high Arctic were present within the ice cover, and subsequently examine the transport pathway that connects the export of thick multiyear sea ice from the Lincoln Sea and Canadian Arctic Archipelago to coastal communities in Newfoundland. We conclude with a discussion on how an increasingly mobile Arctic sea ice cover may increase these ice hazards in the south.

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 score0.119
Threshold uncertainty score0.239

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.020
GPT teacher head0.265
Teacher spread0.246 · 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

Citations55
Published2018
Admission routes3
Has abstractyes

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