Development of a lightweight buoyancy vehicle for upper ocean surveys
Bibliographic record
Abstract
A review of oceanic data collection technologies reveals a predominant focus on upper ocean measurements. There is also a noticeable trend toward autonomous data collection. While traditional profilers are built for extreme pressures and are focused on deep ocean data, we focus on a buoyancy mechanism redesign that aims to create a cost-effective autonomous profiler tailored to the upper ocean. A lightweight and user-friendly upper ocean profiler called the Aurelia Upper Ocean Profiler (UOP), inspired by the Aurelia jellyfish genus, is developed to focus on the top 200 metres of ocean. The proposed buoyancy vehicle is simplified down to bare necessities to create an inexpensive and compact system. It regulates its depth by adjusting its density using an open piston pump and monitoring oceanic pressure differentials. The current design includes wireless communications, a scalable buoyancy engine, and an intuitive interface. The Aurelia UOP’s origins trace back to a 2016 Senior Capstone project led by the first author. In total, the work of five multidisciplinary undergraduate teams and two graduate theses culminated in the Aurelia UOP, a compact 4.5 kg device capable of reaching depths of 50 m. Designed for versatility, it can be programmed via an Android interface using Bluetooth, executing multi-command missions for drifting, profiling, and sensor readings based on user-specified time/depth intervals.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".