MétaCan
Menu
Back to cohort
Record W2776125368 · doi:10.18063/som.v2i2.328

Impacts of model resolution on simulation of meso-scale eddies in the Northeast Pacific Ocean

2017· article· en· W2776125368 on OpenAlexaff
Youyu Lu, Jiaxing Li, Lei Ji, Charles G. Hannah

Bibliographic record

VenueSatellite Oceanography and Meteorology · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsEnvironment and Climate Change CanadaBedford Institute of Oceanography
Fundersnot available
KeywordsEddyAnomaly (physics)LatitudeGeologyClimatologySatelliteLongitudeScale (ratio)GeodesyEnvironmental scienceMeteorologyPhysicsTurbulence

Abstract

fetched live from OpenAlex

The model simulated meso-scale eddies in the Northeast Pacific Ocean, using two models with nominal horizontal resolutions of 1/12° and 1/36° in latitude/longitude (grid spacing of 7.5 km and 2.5 km), respectively, are presented. Compared with the 1/12° model, the 1/36° model obtains (1) similar variance and wave number spectra of the sea level anomaly and water temperature anomaly, and (2) increases in the level of the domain-averaged total kinetic energy, eddy kinetic energy (EKE), and variance of horizontal gradient of water temperature. In the interior basin of the southern region, both models show stronger eddy frontal activities, represented by EKE, temperature and its horizontal gradient, in summer and fall than in winter and spring. The challenge of evaluating the realism of highresolution ocean models with conventional satellite remote sensing observations is discussed.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.242
Teacher spread0.225 · 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 designSimulation or modeling
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

Citations17
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueSatellite Oceanography and MeteorologySame topicOceanographic and Atmospheric ProcessesFrench-language works237,207