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Record W2031147550 · doi:10.1029/2007jc004304

The long‐term and interannual variability of summer fresh water storage over the eastern Siberian shelf: Implication for climatic change

2008· article· en· W2031147550 on OpenAlexafffund
Igor Dmitrenko, Sergey Kirillov, Bruno Tremblay

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsMcGill University
FundersOffice of Polar ProgramsNatural Sciences and Engineering Research Council of CanadaInternational Arctic Research Center, University of Alaska, FairbanksNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsHydrographyOceanographyClimatologyEnvironmental scienceArcticAtmosphere (unit)GeologyDischargeGeographyDrainage basinMeteorology

Abstract

fetched live from OpenAlex

A time series of summer fresh water content anomalies (FWCA) over the Laptev and East Siberian sea shelves was constructed from historical hydrographic records for the period from 1920 to 2005. Results from a multiple regression between FCWA and various atmospheric and oceanic indices show that the fresh water content on the shelves is mainly controlled by atmospheric vorticity on quasi‐decadal timescales. When the vorticity of the atmosphere on the shelves is antycyclonic, approximately 500 km 3 of fresh water migrates from the eastern Siberian shelf to the Arctic Ocean through the northeastern Laptev Sea. When the vorticity of the atmosphere is cyclonic, this fresh water remains on the southern Laptev and East Siberian sea shelves. This FWCA represents approximately 35% of the total fresh water inflow provided by river discharge and local sea‐ice melt, and is about ten times larger than the standard deviation of the Lena River summer long‐term mean discharge. However, the large interannual and spatial variability in the fresh water content of the shelves, as well as the spatial coverage of the hydrographic data, makes it difficult to detect the long‐term tendency of fresh water storage associated with climate change.

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)0.000
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 score0.042
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.044
GPT teacher head0.307
Teacher spread0.263 · 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

Citations114
Published2008
Admission routes2
Has abstractyes

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