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Record W2094297791 · doi:10.5670/oceanog.2009.68

GODAE Systems in Operation

2009· article· en· W2094297791 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOceanography · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsSt. John’s Health Sciences Centre
Fundersnot available
KeywordsDownscalingArgoData assimilationClimatologyAltimeterEnvironmental scienceMeteorologyScale (ratio)Earth system scienceGeologyOceanographyGeographyClimate change

Abstract

fetched live from OpenAlex

During the last 15 years, operational oceanography systems have emerged in several countries around the world. This emergence has been largely fostered by the GODAE experiment, during which each nation engaged in this activity have organised partnership and constructive competition. This trans-national coordination was very beneficial for the development of operational oceanography, leading to economies of scales and more targeted actions. Today, several systems provide routine real-time ocean analysis and forecast and/or reanalysis products. They are all based on (i) state-of-the-art primitive equation baroclinic Ocean General Circulation Model (OGCM) configurations, either global or regional (basin-scale), with resolutions that range from coarse to eddy resolving and (ii) data assimilation techniques whose complexity ranges from simple analysis correction to advanced 4D variational schemes. They assimilate altimeter sea level anomalies, remotely sensed SST such as GHRSST products and in situ profiles of T and S, including ARGO. Some systems have implemented downscaling capacities in specific regions of interest including shelf/coastal seas. Some also have implemented coupling

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.

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 score0.035
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.193
Teacher spread0.186 · 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