Variational Interpolation of Circulation with Nonlinear, Advective Smoothing*
Bibliographic record
Abstract
A modified variational algorithm, previously proposed in meteorology, is presented for the interpolation of oceanic hydrographic and velocity data. The technique is anisotropic and involves a variational approach that allows revealing of the spatial structure in its application. Being a part of the variational family of algorithms, the method is quite general in that it allows one to set dynamical constraints, and weighting functions, applicable to the problem of interest. This flexibility is illustrated by using the nonlinear terms of momentum balance equation as constraints. The inclusion of these constraints appears to assist in the resolution of narrow jets in the flow fields. The method is applied to data from two different regions of the ocean: Lagrangian drifter data from the northwest Pacific and hydrographic data from the Scotian Shelf. Each dataset presents quite different scales, physical processes, and data types. The resulting flow fields are compared with results determined from traditional optimal interpolation, and advantages of the proposed method are discussed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".