Augmented Reality Visualization for Sailboats (ARVS)
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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