Enhanced Mean Dynamic Topography and Ocean Circulation Estimation using GOCE Preliminary Models
Bibliographic record
Abstract
The Gravity and Ocean Circulation Experiment - GOCE satellite mission measure the Earth gravity field with unprecedented accuracy leading to substantial improvements in the modelling of the ocean circulation and transport. In this study of the performance of GOCE, the new preliminary gravity models have been combined with the recent DNSC08MSS mean sea surface model to construct a global GOCE satellite-only mean dynamic topography model. At a first glance, the GOCE MDT display the well known features related to the major ocean current systems. A closer look, however, reveals that the improved gravity provided by the GOCE mission has enhanced the resolution and sharpened the boundary of those features. A computation of MDT slopes clearly displays the improvements in the description of the current systems. In the North Atlantic Ocean, the Gulf Stream is very well defined and the Labrador and the Greenland currents are clearly displayed. Furthermore, different branches of the North Atlantic Current are seen. In the North Pacific Ocean, the Kuroshio and its extension are well recovered, also with its branches. In the Southern hemisphere, both the Aghulas and the South Atlantic current systems are very clearly displayed. In the Antarctic Circumpolar Current system different flow paths are revealed. The results of this preliminary analysis using preliminary GOCE gravity models clearly demonstrate the potential of GOCE mission. Already at this stage the resolution and the estimation of the surface currents have been increased by at least a factor of two compared to similar pre-GOCE satelliteonly studies. Future GOCE models are expected to further enhance studies of the ocean circulation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".