Augmented Reality Visualization for Sailboats (ARVS)
Bibliographic record
Abstract
In order to safely operate sailboats, captains often rely on proper interpretation of several marine aspects to make decisions. In this project, we have developed an Augmented Reality System (ARS) to provide captains of sailboats with a centralized sensor data server and a visualization method. We have deployed an experimental proof-of-concept version of this system on our research vessel, SV Moon shadow. Assistance in navigation is of particular interest for small sailing vessels as they are sometimes sailed by the captain alone. At the same time there are a large number of data inputs such as wind, tide, weather, position, and presence of obstacles such as logs or kelp that have to be considered to choose the proper course of action. We introduce a visualization tool that provides an interface for representing a wide spectrum of relevant marine data. The interface relies on a real-time data server that provides information about the status of the vessel (wind, GPS, gyro, accelerometer, depth sounder etc.) An important component of the interface is a debris detector that analyzes data from a camera mounted on the bow in order to warn a captain about a potential collision. We have also examined initial feedback on this tool from a number of users.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".