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Record W2030543774 · doi:10.1029/2009jc005834

Identification of typical scenarios for the surface Lagrangian residual circulation in the Iroise Sea

2010· article· en· W2030543774 on OpenAlexaff
Héloïse Müller, Bruno Blanke, Franck Dumas, Vincent Mariette

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsCanadian Association for Laboratory Animal Science
FundersUniversité de Bretagne Occidentale
KeywordsResidualLagrangianOcean currentRadarForcing (mathematics)Current (fluid)MeteorologyLagrangian analysisGeologyClimatologyEnvironmental sciencePhysicsOceanographyMathematics

Abstract

fetched live from OpenAlex

This paper describes the surface Lagrangian residual circulation (LRC) over 2.5 day intervals in the Iroise Sea, west of France, and evaluates, for operational purposes, the influence of the different physical mechanisms that govern it. The method consists of the calculation of water displacements with a diagnostic Lagrangian tool that computes the trajectories of numerical particles in a given velocity field. The LRC is inferred from trajectories integrated over five M2 tidal cycles. The analysis is applied to both gridded genuine current measurements and ocean model outputs: the sea surface currents are derived from high‐frequency (HF) radar measurements and from MARS, a 3‐D regional ocean model used here in idealized configurations. To substantiate the analysis, the Lagrangian residual currents are also compared to genuine movements of drifters released in the Iroise Sea in 2005 and 2007. The LRC is mapped for typical scenarios identified from the Lagrangian analysis of HF radar surface currents measured in winter and summer, under weak (<7 m/s) and strong (>10 m/s) wind conditions, and in neap tide and spring tide seasons. Idealized numerical simulations that switch on and off each individual physical process are used to isolate in the LRC the patterns induced by the atmospheric forcing, tides, and density‐driven currents.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.085
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.299
Teacher spread0.272 · 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

Citations25
Published2010
Admission routes1
Has abstractyes

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