Development of Iceberg Profiling Technology
Bibliographic record
Abstract
Abstract Iceberg management on the Grand Banks of Newfoundland, Canada is currently carried out without knowledge of the underwater shape of the iceberg. An iceberg profiling system is being developed to integrate the rapid generation of 3D iceberg shape data with a collection of tools that utilize the data to provide recommendations, intended to improve iceberg management effectiveness. The intent is for the system to be operated by vessel crew with minimal training. The system utilizes a LiDAR and a pole mounted multibeam sonar to profile the iceberg sail and keel, respectively. A vessel equipped with the profiling system circles an iceberg twice to collect a profile, a process that on average requires approximately 15–30 minutes. The data is collected in the form of a point cloud, which must be de-noised and corrected for both drift and rotation of the iceberg. Tools have been developed to assess the stability of the iceberg, and to consider the shape of the iceberg relative to towing net dimensions, to provide guidance to the operator regarding the recommended towing direction to avoid iceberg rolling or net slippage events. Other applications of the profile data include an impact loads analysis tool that determines the distribution of potential iceberg loads in the event of a collision with a given platform, and an operational iceberg drift model that uses the iceberg shape to improve iceberg drift forecasts. Large-scale field programs were carried out in both 2018 and 2019 as part of the development process for the system. Data collected has shown that iceberg characteristics have changed significantly when compared to iceberg profile data collected in the 1980s. For a given iceberg waterline length, the more recent data shows significantly reduced drafts. The 1980s iceberg dataset currently dominates the data used as the basis for assessing iceberg loads on surface facilities and iceberg risk to subsea assets. Reduced iceberg drafts will result in reduced risk to subsea facilities and pipelines. These results and observations demonstrate the usefulness of the iceberg profiling system as an environmental monitoring tool, and the data collected has design and operational applications. The development and capabilities of the system are presented, as well as the comparison of the 1980’s and newer iceberg datasets and implications for iceberg risk to facilities on the Grand Banks and surrounding regions.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".