Bibliographic record
Abstract
A method is presented to calculate mean sea surface dynamic topography from satellite altimeter observations of its temporal variability. Time averaging of a simplified version of the quasi-geostrophic potential vorticity equation for the upper ocean layer results in a differential equation for the averaged relative vorticity in which the mean divergence of the eddy vorticity fluxes acts as a source or sink. The essential part is that these eddy fluxes can be determined from the altimeter observations. Consequently, no parameterisations appear in the averaged vorticity equation. From the average vorticity field, surface geostrophic velocities and related mean dynamic sea surface topography can then simply be derived. The usefulness of the method is established using “perfect” data, namely numerical output from the United Kingdom Fine Resolution Antarctic Model. The method appears applicable to areas of the ocean with strong enough mesoscale variability such as the major western boundary currents and their extensions and to frontal regions of the Antarctic Circumpolar Current. Quite realistic results are presented for active regions of the world ocean. Results are compared with hydrographic observations for the major western boundary current extensions of the Southern Ocean. An important application is to combine the newly derived averaged flow field with the observed eddy field to derive the total time-varying geostrophic surface velocity field. As a striking example this is applied to the Agulhas Current retroflection, where the repeated shedding of large rings can now be synoptically reconstructed as a continuous process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.989 | 0.991 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".