Development of a sensor testbed for Maritime Autonomous Surface Ship situational awareness
Bibliographic record
Abstract
The National Research Council of Canada's Ocean, Coastal and River Engineering Research Centre (NRC-OCRE) and Automotive and Surface Transportation Centre (NRC-AST), along with Transport Canada's Innovation Centre (TC-IC), have been collaborating over the past three years to develop systems and methodologies for investigating sensor performance on marine vehicles. A large portion of this effort has been dedicated to the development of a so-called “Maritime Autonomous Surface Ship (MASS) Sensor Testbed” - a platform for evaluation of various sensors utilized for marine vehicle situational awareness, particularly in the context of harsh environmental conditions. Equipment suitable for deployment on model-scale and full-scale vessels in harsh environments was developed, and was deployed on a physical model-scale Offshore Supply Vessel and on a 5.5-meter Rigid Hull Inflatable Boat (RHIB) designed for autonomous surface research and development. Tank tests were performed in 2021 with a model-scale sensor testbed that informed the development of a full-scale sensor testbed. In 2024 and 2025, field trials were conducted in varying environmental conditions near port infrastructure while docking and maneuvering both in isolation as well as in coordination with an additional autonomous vessel platform (also capturing sensor data).
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".