Vessel Optimal Trajectory and Path Adaptation for Reducing Noise at Cetacean Location Under Velocity Constraints
Bibliographic record
Abstract
Ahstract- This study suggests to improve the vessel noise impact near cetacean. An automatic path planning and indirect vessel velocity adaptation (using propeller revolution per minute) is suggested in order to reduce the noise. A set of layers (labelled as costmap) is suggested, each layer representing a map of the risk, in order to reduce the risk and estimate the optimal path under constraints (minimum travelled distance versus maximum cetacean distance). For adapting the path, one static costmap is used to describe the shores and islands. Inflation layer is used for safety and a conflict costmap is used to represent the cetacean potential locations. Artificial Potential Field is applied on the combined costmaps (master costmap) and then two different path planning algorithms are applied and compared. Over the preferred path, a dynamic simulation including autopilot to follow waypoints from the path planner, noise synthesis and velocity adaptation is detailed. This navigation aid system suggests to the pilot new adapted path and vessel velocity considering environmental constraints.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".