MétaCan
Menu
Back to cohort
Record W1895913850 · doi:10.1175/jpo-d-15-0078.1

Modelled Variations of Deep Convection in the Irminger Sea during 2003–10

2015· article· en· W1895913850 on OpenAlexaffabout
Jean-Philippe Paquin, Youyu Lu, Simon Higginson, Frédéric Dupont, Gilles Garric

Bibliographic record

VenueJournal of Physical Oceanography · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsBedford Institute of OceanographyDalhousie University
Fundersnot available
KeywordsConvectionGeologyIsopycnalOcean gyreClimatologyThermohaline circulationDeep convectionWater massForcing (mathematics)Deep ocean waterAtmospheric sciencesOceanographyMeteorologySubtropics

Abstract

fetched live from OpenAlex

Abstract Results from a high-resolution ice–ocean model are analyzed to understand the physical processes responsible for the interannual variability of ocean convection over the Irminger Sea. The modeled convection in the open Irminger Sea for the winters of 2007/08 and 2008/09 is in good agreement with observations. Deep convection is caused by strong atmospheric forcing that increases the ocean heat loss through latent and sensible heat fluxes. Greenland tip jets are found to be the only strong wind events that directly affect the deep convection area and explain up to 53% of the total turbulent heat loss during active convection years. Deep convection is modeled where there is favorable preconditioning of the water column due to isopycnal doming inside the semienclosed Irminger Gyre. The region of deep convection is also characterized by weak eddy kinetic energy. Finally, an estimation of the surface-forced water mass transformation confirms the Irminger Sea as a region of intermittent production of Labrador Sea Water, with annual averages between 0.9 and 1.9 Sverdrups (Sv; 1 Sv ≡ 10 6 m 3 s −1 ) of water denser than 27.7 kg m −3 for years of active convection.

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)0.000
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 score0.158
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.013
GPT teacher head0.217
Teacher spread0.204 · 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

Citations8
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Physical OceanographySame topicOceanographic and Atmospheric ProcessesFrench-language works237,207