MétaCan
Menu
Back to cohort
Record W2560414194 · doi:10.5194/os-13-837-2017

A modelling study of eddy-splitting by an island/seamount

2017· article· en· W2560414194 on OpenAlexfundno aff
Shengmu Yang, Jiuxing Xing, Daoyi Chen, Shengli Chen

Bibliographic record

VenueOcean science · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
FundersFisheries and Oceans CanadaUniversitetet i BergenChina Postdoctoral Science Foundation
KeywordsEddySeamountGeologyMesoscale meteorologyRADIUSVorticityPotential vorticityClimatologyOceanographyGeophysicsMeteorologyPhysicsVortexTurbulence

Abstract

fetched live from OpenAlex

Abstract. A mesoscale eddy's trajectory and its interaction with topography under the planetary β and nonlinear effects in the South China Sea are examined using the MIT General Circulation Model (MITgcm). Warm eddies propagate to the southwest while cold eddies propagate to the northwest. The propagation speed of both warm and cold eddies is about 2.4 km day−1 in the model. The eddy trajectory and its structure are affected by an island or a seamount, in particular, under certain conditions, the eddy may split during the interaction with an island/seamount. We focus this research on two parameters R and S (where R and S are two dimensionless parameters of the island size and submergence depth; R is the ratio of the island radius to the eddy radius, and S is the ratio of the seamount submergence depth to the eddy vertical length). The results of sensitivity experiments with varying island or seamount geometry indicate that the eddy would split in the qualitative range of 1∕4 < R < 2 and S < 1∕5. The scale of the secondary eddy split-off decreases as the island diameter or the seamount submergence depth increases. In the splitting process, besides the off-spring eddy, there are also some filaments or eddies with opposite vorticity appearing around the eddy. Eddy-splitting, therefore, is an important way to transform energy from the mesoscale to sub-mesoscale in the ocean.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.022
GPT teacher head0.252
Teacher spread0.230 · 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.

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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueOcean scienceSame topicOceanographic and Atmospheric ProcessesFrench-language works237,207