A Revised Basis for Iceberg Areal Density Values for Risk Analysis
Bibliographic record
Abstract
Abstract Iceberg areal density is one of the most important and challenging parameters to define accurately for offshore petroleum exploration. The two data sources for iceberg areal density considered here are aerial reconnaissance data collected by the International Ice Patrol (IIP) and iceberg charts which merge aerial reconnaissance data with other observations and model output. The IIP operates regular flights to monitor iceberg hazards to transatlantic transportation off the Canadian East Coast. The IIP and Canadian Ice Service (CIS) work together to generate daily ice charts year-round to provide the most reliable and timely information about the iceberg distribution by defining an iceberg limit to minimize risk of iceberg collision to transportation. The purpose of the iceberg charts is to promote safe maritime operations and to inform mariners about the latest ice conditions in navigable Canadian waterways and transatlantic shipping lanes in international waters. With navigational safety as its primary goal, the IIP develops the iceberg limit and distribution for vessels planning to avoid encountering icebergs completely. These warnings therefore are generally more conservative than on-site observations. The daily ice chart is created based on the data provided by various sources and is modified regularly by adding new sightings and applying drift and deterioration models to previous sightings. Among all the sources, aerial reconnaissance provides the most up-to-date information on iceberg conditions, and are generally conducted between February and July. For a better understanding of the influence of the data sources, iceberg frequency values using aerial reconnaissance data and charts were compared for a common period of time for several locations. Comparing the results, it was observed that results from aerial reconnaissance data analysis are typically lower than results from chart data i.e., more icebergs were reported in the ice charts than were sighted by aerial reconnaissance. This is consistent with IlP's conservative approach in reporting iceberg hazards to transatlantic mariners. Using the most appropriate source of data to identify the risk that icebergs pose for offshore petroleum production facilities is essential. The objective of this paper is to assess the discrepancies between data provided through aerial reconnaissance and that included in the daily iceberg charts.
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 | 0.009 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".