GODAE OceanView Inter-comparison for the Australian Region
Bibliographic record
Abstract
This paper compares the performance of short-range operational ocean forecasts, using ‘observational space’ metrics developed under GODAE OceanView (GOV). Best estimates (behind the real-time analysis) and forecasts are inter-compared for the Australian region (0-50S, 90-180E) for 2013. Systems considered include those developed in Australia, France, Canada, United Kingdom and USA. Each system is compared to observations of along-track sea level anomaly, sea surface temperature observations from surface drifters and sub-surface Argo profiles of temperature and salinity. The UK operational system generally has the smallest errors for sea surface temperature and sea level anomaly for the Australian region. However, the French systems outperform others in sub-surface temperature and salinity for the region. Of the two products provided by the Australian centre, an ensemble based approach is found to perform better than the deterministic system, having higher skill and lower root mean square errors. Some of the ‘better’ results of systems can be attributed in part to the lack of independence of the reference observations; however the study does demonstrate the feasibility and robustness of GOV global ocean inter-comparison efforts for regional applications.
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.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".