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Record W2551373400 · doi:10.1002/2016gl071489

The influence of declining sea ice on shipping activity in the Canadian Arctic

2016· article· en· W2551373400 on OpenAlexafffundabout
Larissa Pizzolato, Stephen Howell, Jackie Dawson, Frédéric Laliberté, Luke Copland

Bibliographic record

VenueGeophysical Research Letters · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of OttawaEnvironment and Climate Change Canada
FundersNatural Sciences and Engineering Research Council of CanadaNational Oceanic and Atmospheric Administration
KeywordsBaySea iceBeaufort seaArcticOceanographyArctic ice packArctic sea ice declineCanada BasinBeaufort scaleClimatologyGeologyEnvironmental sciencePhysical geographyAntarctic sea iceGeography

Abstract

fetched live from OpenAlex

Abstract Significant attention has focused on the potential for increased shipping activity driven by recently observed declines in Arctic sea ice cover. In this study, we describe the first coupled spatial analysis between shipping activity and sea ice using observations in the Canadian Arctic over the 1990–2015 period. Shipping activity is measured by using known ship locations enhanced with a least cost path algorithm to generate ship tracks and quantified by computing total distance traveled in kilometers. Statistically significant increases in shipping activity are observed in the Hudson Strait (150–500 km traveled yr −1 ), the Beaufort Sea (40–450 km traveled yr −1 ), Baffin Bay (50–350 km traveled yr −1 ), and regions in the southern route of the Northwest Passage (50–250 km traveled yr −1 ). Increases in shipping activity are significantly correlated with reductions in sea ice concentration (Kendall's tau up to −0.6) in regions of the Beaufort Sea, Western Parry Channel, Western Baffin Bay, and Foxe Basin. Changes in multiyear ice‐dominant regions in the Canadian Arctic were found to be more influential on changes to shipping activity compared to seasonal sea ice regions.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.167
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.292
Teacher spread0.255 · 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

Citations181
Published2016
Admission routes3
Has abstractyes

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