GODAE Systems in Operation
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.194 | 0.121 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".