Adaptive AUV Mission Control System Tested in the Waters of Baffin Bay
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The primary objectives of this paper are to test an adaptive sampling method for an autonomous underwater vehicle, specifically tailored to track a hydrocarbon plume in the water column. An overview of the simulation of the developed applications within the autonomous system is presented together with the subsequent validation achieved through field trials in an area of natural oil seeps near to Scott Inlet in Baffin Bay. This builds upon our prior published work in methodological development. The method employed involves an integrated backseat drive of the AUV, which processes in situ sensor data in real time, assesses mission status, and determines the next task. The core of the developed system comprises three modular components—Search, Survey, and Sample—each designed for independent and sequential execution. Results from tests in Baffin Bay demonstrate that the backseat drive operating system successfully accomplished mission goals, recovering water samples at depths of 20 m, 50 m, and 200 m before mission completion and vehicle retrieval. The principal conclusion drawn from these trials underscores the system’s resilience in enhanced decision autonomy and validates its applicability to marine pollutant assessment and mitigation.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it