A description of existing Operational Ocean Forecasting Services around the Globe
Bibliographic record
Abstract
Abstract. Predicting the ocean state in support of human activities, environmental monitoring and policymaking across different regions worldwide is fundamental and require numerical strategies that have to address their physical peculiarities. The Authors provide an outlook on the status of operational ocean forecasting systems in 8 key regions in the world ocean: the West Pacific and Marginal Seas of South and East Asia, the Indian Ocean, the African Seas, the Mediterranean and Black Sea, the North-East Atlantic, the South and Central America Seas, the North America and the Arctic. Starting from the specific regional challenges to address, the Authors discuss on the numerical strategy and available operational systems, pointing out the straightness and the ways forward to improve the essential ocean variables predictability from regional to coastal scales, products reliability and accuracy. This compendium is a baseline to understand the worldwide offer, showing how the heterogeneity of the physical characteristics of ocean dynamics can be addressed thanks to a systematic and regular provisioning of predictions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.021 |
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".