Improving path-planning for glider operations: A current-forecast based approach applied in the Gulf of St. Lawrence
Bibliographic record
Abstract
Ocean gliders are a key platform that can fill the gaps between coastal and open ocean observing systems, between Argo floats, and moorings and ship-based strategies (Testor et al., 2019). One challenge for these slow-moving, primarily underwater systems, is to improve waypoint-based navigation to minimize the effects of wind and current driven dynamics. This is important in time-critical applications where there are advantages in reaching a site as quickly as possible, for example when monitoring storm systems or tracking eddies. Optimal path planning will also be important in long duration missions where battery consumption is a limiting factor of the deployment. In August 2019, a Slocum glider was deployed in the Gulf of St. Lawrence for preliminary system studies. During the deployment, a waypoint planning system was used to generate the glider waypoints list files. In this presentation, we will present the design of the path-planning system and show in-situ scientific measurements collected by the glider. The key optimized value assigned to enable path planning are minimizing current speeds and the key metric for validating the performance is the distance covered per hour. This approach has tremendous value for improving the autonomy of gliders in operational ocean monitoring applications, removing pressure from pilots managing the glider mission and improving the state-of-the-art of ocean data products.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".