A description of existing operational ocean forecasting services around the globe
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.
Bibliographic record
Abstract
Abstract. Predicting the ocean state in support of human activities, environmental monitoring, and policymaking across different regions worldwide is fundamental. To properly address physical, dynamical, ice, and biogeochemical processes, numerical strategies must be employed. The authors provide an outlook on the status of operational ocean forecasting systems in eight key regions including the global ocean: the West Pacific and Marginal Seas of South and East Asia, the Indian Seas, the African Seas, the Mediterranean and Black Sea, the North East Atlantic, South and Central America, North America (including the Canadian coastal region, the United States, and Mexico), and the Arctic. The authors initiate their discussion by addressing the specific regional challenges that must be addressed and proceed to discuss the numerical strategy and the available operational systems, ranging from regional to coastal scales. This compendium serves as a foundational reference for understanding the global offering, demonstrating how the diverse physical environment – ranging from waves to ice – and the biogeochemical features besides ocean dynamics can be systematically addressed through regular, coordinated prediction efforts.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it