The Joint IOC (of UNESCO) and WMO Collaborative Effort for Met-Ocean Services
Bibliographic record
Abstract
The Joint Committee for Oceanography and Marine Meteorology (JCOMM), a joint technical commission of IOC of UNESCO and WMO, has devised a coordination mechanism for the fit-for-purpose delivery of an end-to-end system, from ocean observations to met-ocean operational services. This paper offers a complete overview of the activities carried out by JCOMM and the status of the achievements up to 2017. The JCOMM stakeholders are the WMO Members and the IOC Member States, their research and operational Institutions, which mandated JCOMM to devise an international strategy to advance toward the achievement of the United Nations Sustainable Development Goals. The three activity areas, namely the Observation Program Area-OPA, the Data Management Program Area-DMPA and the Services and Forecasting Services Program Area-SFSPA have established several expert teams to contribute to the international coordination. OPA is organized in observing networks connected with different observing technologies, DMPA organizes the overall near-real time and delayed mode data assembly and delivery methodology and architecture and the SFSPA coordinates the met-ocean services stemming out of observations and data management. The future developments should strengthen the coordination in the three program areas considering the inclusion of new and emergent observing technologies, the interoperability of met-ocean data assembly centers and the establishment of efficient research to operations protocols, as well as better fit-for-purpose customized services for the public and private sectors.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".