Development of a semi-submersible unmanned surface craft
Bibliographic record
Abstract
Under the Ice Ocean Sentinel System (IOSS) project the Autonomous Ocean Systems Lab (AOSL) of Memorial University has begun the process of producing a prototype vessel utilizing a semi-submersible single strut hull form. The prototype has been designed to provide tunable stability characteristics, low relative heave motions, and increased overall survivability when compared to current lab scale autonomous surface craft platforms. The vehicle's primary mission will be to operate as a deployment platform for oceanographic data collection and ice reconnaissance. The secondary purpose of the prototype is to provide proof of concept and performance data which will be used to validate the developed numerical models. The prototype vehicle will also provide valuable lessons learned to be applied for a full scale vessel design. Although the vehicle has been developed as a large scale model, it has been outfitted with a full electronics system including controls, sensors and payloads. This prototype replaces the traditional tank testing of a model generally associated with new hull designs. The design of the hull, launch and recovery considerations, and propulsion power estimates are discussed. The paper also describes the process through which the design was completed, the current status of the project, and the expected outcomes of the prototype development project.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".