Mapping the underside of an iceberg with a modified underwater glider
Bibliographic record
Abstract
Abstract Icebergs pose many challenges to offshore operations in the Arctic Ocean and sub‐arctic regions. They could damage underwater infrastructure such as pipelines, and disrupt marine transportation. The below‐water shape of an iceberg is a key factor for iceberg management in the North Atlantic Ocean because it affects the iceberg towing plans and iceberg drift patterns. In recent years, unmanned platforms have been proposed as potential candidates for underwater iceberg mapping. Compared to a conventional ship‐based iceberg survey, using unmanned platforms is more efficient and safer. In this paper, we present research using a hybrid underwater glider to measure the underwater shape of an iceberg. The vehicle is equipped with a mechanical scanning sonar for range sensing and iceberg mapping, and a guidance system is designed to use the sonar measurements for guiding the vehicle to circumnavigate an iceberg at the desired standoff distance. Several field experiments have been conducted on an iceberg to evaluate the system performance. With repeated observations, the underside of the target iceberg was successfully reconstructed, and iceberg shape comparisons are presented.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".