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Record W2039841428 · doi:10.1175/2011jpo4606.1

Mooring-Based Observations of Double-Diffusive Staircases over the Laptev Sea Slope*

2011· article· en· W2039841428 on OpenAlexaff
Igor V. Polyakov, Andrey V. Pnyushkov, Robert Rember, Vladimir Ivanov, Yueng‐Djern Lenn, Laurie Padman, Eddy C. Carmack

Bibliographic record

VenueJournal of Physical Oceanography · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsFisheries and Oceans Canada
FundersNatural Environment Research CouncilSight Research UK
KeywordsIsopycnalHaloclineGeologyMooringThermoclineArcticEddyHeat fluxOceanographyClimatologyAtmospheric sciencesHeat transferSalinityTurbulenceMeteorologyMechanics

Abstract

fetched live from OpenAlex

Abstract A yearlong time series from mooring-based high-resolution profiles of water temperature and salinity from the Laptev Sea slope (2003–04; 2686-m depth; 78°26′N, 125°37′E) shows six remarkably persistent staircase layers in the depth range of ~140–350 m encompassing the upper Atlantic Water (AW) and lower halocline. Despite frequent displacement of isopycnal surfaces by internal waves and eddies and two strong AW warming pulses that passed through the mooring location in February and late August 2004, the layers preserved their properties. Using laboratory-derived flux laws for diffusive convection, the authors estimate the time-averaged diapycnal heat fluxes across the four shallower layers overlying the AW core to be ~8 W m−2. Temporal variability of these fluxes is strong, with standard deviations of ~3–7 W m−2. These fluxes provide a means for effective transfer of AW heat upward over more than a 100-m depth range toward the upper halocline. These findings suggest that double diffusion is an important mechanism influencing the oceanic heat fluxes that help determine the state of Arctic sea ice.

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 score0.030
Threshold uncertainty score0.060

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 communication0.0000.000
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 head0.033
GPT teacher head0.228
Teacher spread0.195 · 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

Citations72
Published2011
Admission routes1
Has abstractyes

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