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Record W4285306235 · doi:10.5670/oceanog.2022.124

Air-Ice-Ocean Interactions and the Delay of Autumn Freeze-Up in the Western Arctic Ocean

2022· article· en· W4285306235 on OpenAlexaboutno aff
Jim Thomson, Maddie Smith, Kyla Drushka, Craig R. Lee

Bibliographic record

VenueOceanography · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersOffice of Naval ResearchNuclear Safety and Security CommissionNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsArcticSea iceArctic ice packArctic sea ice declineDrift iceOceanographyClimatologyEnvironmental scienceLead (geology)Pelagic zoneGeologyCanada Basin

Abstract

fetched live from OpenAlex

Arctic sea ice is becoming a more seasonal phenomenon as a direct result of global warming. Across the Arctic, the refreezing of the ocean surface each autumn now occurs a full month later than it did just 40 years ago. In the western Arctic (Canada Basin), the delay is related to an increase in the seasonal heat stored in surface waters; cooling to the freezing point requires more heat loss to the atmosphere in autumn. In the marginal ice zone, the cooling and freezing process is mediated by ocean mixing and by the presence of remnant sea ice, which may precondition the ocean surface for refreezing. The delay in refreezing has many impacts, including increased open ocean exposure to autumn storms, additional wave energy incident to Arctic coasts, shifts in animal migration patterns, and extension of the time window for transit by commercial ships along the Northern Sea Route. This article reviews the observed trends in the western Arctic and the processes responsible for these trends, and provides brief in situ observations from the Beaufort Sea that illustrate some of these processes.

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)0.000
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 score0.086
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.008
GPT teacher head0.205
Teacher spread0.196 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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