A Hindcast Model for Evaluation of Iceberg Management Operations
Bibliographic record
Abstract
Abstract When planning oil and gas exploration and production operations off the east coast of Canada, the potential for iceberg impacts must be considered. Environmental conditions in this region can be very harsh, and iceberg trajectories are notably unpredictable. When an iceberg has the potential to impact a Floating Production, Storage, and Offloading (FPSO) platform, ice management through towing will be attempted; and if this fails, the production system will be shut down, line flushed, the mooring and riser systems disconnected, and the platform moved off site. If trajectory forecasting were highly accurate, only icebergs passing very close to the platform would require ice management and possible shutdown of the platform. Given natural variations in wind, currents, and waves, and challenges measuring and forecasting these parameters, there is considerable forecast uncertainty. This results in added expenses for extra ice management and unnecessary shutdowns. Improvements in trajectory forecasting accuracy, characterization of forecast uncertainty, and methods to account for these uncertainties in operations would all be beneficial. This paper outlines an approach for simulating large numbers of iceberg trajectories in varied and realistic environmental conditions from hindcast met-ocean data in conjunction with a forecasting uncertainty model derived from forecast validation studies. A model, named BergCast, was developed so that proposed strategies for improving ice management operations can be evaluated, and the value of reducing forecasting uncertainty quantified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".