Ship Collisions with Icebergs: An Historical Record of Collisions in the Seas Around North America and Greenland
Bibliographic record
Abstract
A database of over 560 incidents of ship collisions with icebergs has been compiled. Most of these collisions occurred in the North Atlantic but there are also several from around Greenland, the Canadian Arctic and sub-Arctic, and from the fiords of Alaska. The database nominally covers a 200 year period from about 1800 to present, and was compiled from contemporary shipping newspapers and gazettes. It contains such information as the name of the vessel, geographic location, and other factors when known such as vessel speed, iceberg size, damage and loss of life. The long term trend of collisions with icebergs on and around the Grand Banks correlates well, for the most part, with the re-constructed sea ice records off the east coast. The decades around 1890 were unusually severe in ice conditions and this is reflected in the number of casualties. Correlation between the two data sets becomes increasingly less apparent throughout the 20th century and this is likely due to better iceberg monitoring and detection methods. Incidents still occur at an average of 1 to 2 per year and still pose a threat to operators and navigators on the Grand Banks where oil resources are being increasingly developed. This paper discusses the trends in collisions. The database itself is available from the author or from the web page, http://www.nrc.ca/imd/ice/ and will become available in a forthcoming International Ice Patrol Bulletin.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".