Development of a New Operational Iceberg Drift Forecast Model for the Grand Banks of Newfoundland
Bibliographic record
Abstract
Abstract During July 2018, an expedition was carried out offshore northern Newfoundland to central Labrador to profile, track, and forecast the drift of icebergs. One of the central goals of the drift modelling work was to test potential improvements in iceberg drift forecast accuracy up to 24 hours when measured iceberg profiles are used as opposed to estimated iceberg draft and mass. During the expedition, 14 icebergs were profiled using a rapid iceberg profiling system which uses a multibeam for the underwater portion of the iceberg and a LiDAR for the freeboard. The 14 icebergs were tracked on the vessel marine radar, and their drift was forecast using a physical model which time integrates the momentum balance of the forces acting on the iceberg. The iceberg profiles were three-dimensional point clouds which provided a highly accurate representation of the iceberg dimensions and shape, and from which a volume and mass could be readily calculated. The point cloud was projected into a two-dimensional plane from 16 perspective angles and averaged into a single projection of iceberg keel and freeboard against which the currents and winds were forced in the drift model, respectively. Average results for the forecast iceberg position versus observed at 24 hours show approximately a nearly 3 km or 18% improvement when iceberg profiles are incorporated into the drift model as opposed to using estimated iceberg draft, shape, and mass. The drift model will become part of an integrated ice profiling, forecasting, and management system for oil and gas exploration and drilling operations on the Grand Banks offshore Newfoundland.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".