Automated Glider Tracking of a California Undercurrent Eddy Using the Extended Kalman Filter
Bibliographic record
Abstract
Abstract Automated feature tracking and vehicle navigation have the potential to facilitate autonomous surveys of ocean eddies by increasing sampling quality and/or decreasing operator workload. During an observational campaign in late 2013 and early 2014, methods for automated tracking were used to direct multiple ocean gliders during persistent surveys of a California Undercurrent eddy in Washington and British Columbia, Canada, coastal waters over a 3-month period. Glider observations of depth-averaged currents in the ocean’s upper kilometer and vertical separation of selected isopycnals were assimilated into a simple model describing eddy position, size, strength, and background flows using an extended Kalman filter. Though differing in detail from observations, results show the assumed eddy structure was sufficient to describe its essential characteristics and stably estimate eddy position through time. Forecast eddy positions and currents were used to select targets automatically to guide multiple gliders along transects through the eddy center as it translated. Transects performed under automated navigation had comparable or better straightness and distance from the eddy center when compared to navigation based on manual interpretation of the eddy scale and position. The tracking results were relatively insensitive to model choices at times when the eddy was well sampled, but they were more sensitive during sampling gaps and redundancies or rapid eddy translation. Overall, the results provide evidence that automated tracking and navigation are feasible with potential for widespread application in autonomous eddy surveys.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".