A Backseat Control Architecture for a Slocum Glider
Bibliographic record
Abstract
Adaptive sampling provides an innovative and favorable method of improving the effectiveness of underwater vehicles in collecting data. Adaptive sampling works by controlling an underwater vehicle by using measurements from sensors and states of the vehicle. A backseat driver system was developed in this work and installed on a Slocum glider to equip it with an ability to perform adaptive sampling tasks underwater. This backseat driver communicated with the main vehicle control system of the glider through a robot operating system (ROS) interface. The external control algorithms were implemented through ROS nodes, which subscribed simulated sensor measurements and states of the glider and published desired states to the glider. The glider was set up in simulation mode to test the performance of the backseat driver as integrated into the control architecture of the glider. Results from the tests revealed that the backseat driver could effectively instruct the depth, heading, and waypoints as well as activate or deactivate behaviors adaptively. The developed backseat driver will be tested in future field experiments with sensors included and safety rules implemented before being applied in adaptive sampling missions such as adaptive oil spill sampling.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".