Surveying a Floating Iceberg With the USV SEADRAGON
Bibliographic record
Abstract
The calving, drifting, and melting of icebergs has local, regional, and global implications. Besides the impacts to local ecosystems due to changes in seawater salinity and temperature, the freshwater influx and transport can have significant regional effects related to the ocean circulation. The increased influx of freshwater ice due to increase calving from ice shelves and the destabilization of the continental ice sheet will affect sea levels globally. In addition, drifting icebergs pose threats to offshore operations because they could damage offshore installations, e.g., pipelines and subsea manifolds, and interrupt marine transportation. Iceberg drift and deterioration models have been developed to better predict climate change and protect offshore operations. Iceberg shape is one of the most critical parameters in these models, but it is challenging to obtain because of iceberg movement caused by winds, waves, and currents. In this paper, we present an algorithm for iceberg motion estimation and shape reconstruction based on in-situ point cloud measurements. The algorithm is developed based on point cloud matching strategies, policy-based optimization, and Kalman filtering. A down-sampling method is also integrated to reduce the processing time for possible real-time applications. The motion estimation algorithm is applied to a simulated data set and field measurements collected by an Unmanned Surface Vehicle (USV) on a free-floating, translating, and rotating, iceberg. In the field data, the above-water iceberg surface was measured with a scanning LIDAR, while the below-water portion (0–50 m) was profiled using a side-looking multi-beam sonar. When applying the motion estimation algorithm to these two independent point cloud measurements collected by the two sensing modalities, consistent iceberg motion estimates are obtained. The resulting motion estimates are then used to reconstruct the iceberg shape. During the field experiment, additional oceanographic measurements, such as temperature, ocean current, and wind, were collected simultaneously by the USV. We have observed water upwelling and a colder and fresher water plume at the sea surface downstream the iceberg. Combining the iceberg shape rendering and the surrounding environmental measurements, we estimated the iceberg melting parameters due to the sensible heat flux and surface wave erosion at different iceberg sections.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".