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Record W4308025962 · doi:10.1029/2022gl101472

Abrupt Northern Baffin Bay Autumn Warming and Sea‐Ice Loss Since the Turn of the Twenty‐First Century

2022· article· en· W4308025962 on OpenAlexaff
Thomas J. Ballinger, G. W. K. Moore, Yarisbel Garcia‐Quintana, Paul G. Myers, Amreen A. Imrit, Dániel Topál, Walter N. Meier

Bibliographic record

VenueGeophysical Research Letters · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of AlbertaUniversity of Toronto
Fundersnot available
KeywordsBayArctic ice packSea iceOceanographyGeologyArcticCryosphereClimatologyAntarctic sea iceDrift iceFast ice

Abstract

fetched live from OpenAlex

Abstract A delay in autumn sea ice formation is an important consequence of Arctic Amplification. Baffin Bay is one such region impacted by delayed ice formation, though spatiotemporal analyses to date have not detailed the evolution and drivers of such autumn ice changes. In this study, we document abrupt Baffin Bay sea ice cover changes in the key transition month of October from 1950 to 2021. The ice cover mean and variance dramatically change from 2001 onward with a transition to largely ice‐free conditions in the northeast and thinner ice in the northwest. Ocean model experiments attribute these changes to warming of the Atlantic‐origin water (AOW) flowing into northeastern Baffin Bay from the south. Transport and upwelling of this above‐freezing AOW has stunted ice formation in this area, while the basin's cyclonic surface current has contributed to reduced cooling and ice formation in the northwestern portion of Baffin Bay.

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 score0.166
Threshold uncertainty score0.331

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 studies0.0010.001
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 head0.014
GPT teacher head0.240
Teacher spread0.227 · 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

Citations17
Published2022
Admission routes1
Has abstractyes

Explore more

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