Initial performance analysis on underside iceberg profiling with autonomous underwater vehicle
Bibliographic record
Abstract
Off the coast of Newfoundland and Labrador, Canada, ice management is frequently necessary. The operation monitors icebergs' positions, and manages threats posed by icebergs in the vicinity of offshore installations. In this paper, we present a performance analysis of different iceberg underwater profiling strategies using Autonomous Underwater Vehicles (AUVs), such as underwater gliders. Two strategies for profiling an iceberg with AUVs are proposed. The performance and outcomes are initially analysed in a simulation environment based on MATLAB. The iceberg shape used in the simulation derived from real-world iceberg measurements is provided by the National Research Council Canada. The waypoints in simulations are generated autonomously with a sonar model using a modified 3D Ramer-Douglas-Peucker (RDP) algorithm. The modified RDP filter is programmed to extract the feature-significant data in the points cloud and radially expand them with a standoff distance. Simulation results of spiral and vertical-transit profiling mode are included and compared. The sonar model will be implemented with stochastic estimation, and the collision avoidance features will be developed in the vehicle dynamic section. We are integrating a Tritech Micron mechanical scanning sonar into a Slocum underwater hybrid glider for field verification of this modeling.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".