Development of a New Operational Iceberg Drift Forecast Model for the Grand Banks of Newfoundland
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it