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Record W2995237608 · doi:10.1029/2019gl085990

Biophysical Consequences of a Relaxing Beaufort Gyre

2019· article· en· W2995237608 on OpenAlexaboutno aff
Jinlun Zhang, Yvette H. Spitz, Michael Steele, Carin J. Ashjian, Robert G. Campbell, Axel Schweiger

Bibliographic record

VenueGeophysical Research Letters · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersClimate Program OfficeOffice of Polar ProgramsOffice of Naval ResearchNational Aeronautics and Space AdministrationNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsDownwellingUpwellingOceanographyBiomass (ecology)Ocean gyreEnvironmental scienceProductivityArcticNutrientGeologyFisheryEcologySubtropicsBiology

Abstract

fetched live from OpenAlex

Abstract A biophysical model shows that Beaufort Gyre (BG) intensification in 2004–2016 is followed by relaxation in 2017–2018, based on a BG variability index. BG intensification leads to enhanced downwelling in the central Canada Basin (CCB) and upwelling along the coast. In the CCB, enhanced downwelling reduces nutrients, thus lowering primary productivity (PP) and plankton biomass. Enhanced upwelling along the coast and in parts of the Chukchi shelf/slope increases nutrients, leading to elevated PP/biomass in the Pacific Arctic Ocean (PAO) outside of the CCB. The overall PAO PP/biomass is dominated by the shelf/slope response and thus increases during BG intensification. As the BG relaxes in 2017–2018, these processes largely reverse, with increasing PP/biomass in the CCB and decreasing PP/biomass in most of the shelf/slope regions. Because the shelf/slope regions are much more productive than the CCB, BG relaxation has the tendency to reduce the overall production in the PAO.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.218
Threshold uncertainty score0.433

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.025
GPT teacher head0.276
Teacher spread0.251 · 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
Published2019
Admission routes1
Has abstractyes

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