A state‐space model for ocean drifter motions dominated by inertial oscillations
Bibliographic record
Abstract
Coincident ocean drifter position and surface wind time series observed on hourly timescales are used to estimate upper ocean dissipation and atmosphere‐ocean coupling coefficients in the Labrador Sea. A discrete‐process model based on finite differences is used to regress ocean accelerations on ocean velocity estimates but fails because errors in the discrete approximations for the ocean velocities are biased and accumulate over time. Model identification is achieved by fitting a stochastic differential equation model based on classical upper ocean physics to the drifter data via the Kalman filter. Ocean parameters are shown to be nonidentifiable in a direct application to the Labrador Sea data when the known Coriolis parameter is not identified by the model. To address this, the ocean parameters are estimated in an empirical sequence. Data from the Ocean Storms experiment are used to estimate ocean dissipation in isolation from complexities introduced by strong and variable winds. Given a realistic estimate of the ocean dissipation, a second application in the Labrador Sea successfully estimates atmosphere‐ocean coupling coefficients and reproduces the Coriolis parameter. Model assessments demonstrate the robustness of the parameter estimates. The model parameter estimates are discussed in comparison with Ekman theory and results from analyses of the global ocean surface drifter data set.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".