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Record W2981543050 · doi:10.1029/2019gl084652

Episodic Extrema of Surface Stress Energy Input to the Western Arctic Ocean Contributed to Step Changes of Freshwater Content in the Beaufort Gyre

2019· article· en· W2981543050 on OpenAlexaboutno aff
Wenli Zhong, Jinlun Zhang, Michael Steele, Jinping Zhao, Tao Wang

Bibliographic record

VenueGeophysical Research Letters · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersClimate Program OfficeOffice of Naval ResearchNational Key Research and Development Program of ChinaChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsOcean gyreArcticOceanographyGeologyGeostrophic currentClimatologySea iceBeaufort seaCanada BasinArctic ice packOcean currentWind stressEnvironmental scienceAtmospheric sciencesEcology

Abstract

fetched live from OpenAlex

Abstract The recent dramatic decline of sea ice in the western Arctic Ocean changes the transfer of momentum across the ice‐ocean boundary layer. The surface stress energy input through the surface geostrophic current in the Beaufort Gyre (BG) based on a numerical model is 0.03 mW/m2 in 1992–2004 versus 0.23 mW/m2 in 2005–2017. This energy input is primarily concentrated over the southern Canada Basin and the Chukchi Sea. It is 1.38 × 1016 J in observations versus 4.90 × 1016 J in the model in the BG during 2003–2014. We find that some well‐known freshwater changes in the BG over 1992–2017 resulted from episodic extrema of energy input in 2007, 2012, and 2016. In particular, most of the energy input in 2007 was transformed into potential energy (57%) which resulted in a new state of freshwater budget. Our study suggests that as of 2016, the BG had not yet reached a saturated freshwater state.

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 score0.094
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.032
GPT teacher head0.258
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

Citations26
Published2019
Admission routes1
Has abstractyes

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