Analysis of Changing Levels of Ice Strengthening (Ice Class) among Vessels Operating in the Canadian Arctic over the Past 30 Years
Bibliographic record
Abstract
Climate change is impacting sea ice extent and thickness in the Canadian Arctic, creating an increase in maritime accessibility that may accentuate risks related to ship operations due to a related increase in sea ice mobility. The overall risk to ships operating in regions with mobile sea ice will vary significantly depending on the ice class (i.e., level of ice strengthening) of the vessel. Several studies have examined the implications of sea ice change for ship operations, but to date limited analysis has been conducted to understand whether levels of ice strengthening are changing among vessels operating in the Arctic. To address this research gap, more than 100,000 ship position reports covering a 30-year time-period were obtained from the Canadian Coast Guard in order to evaluate changes in shipping activities across Arctic Canada by vessel ice class. Between 1990 and 2019, there has been a substantial reduction in the number of highly strengthened PC3 ships (25%) and a large increase in the number of medium-strengthened PC7 (605%) and low-strengthened 1B (180%) vessels. These trends are particularly acute for certain vessel types, including bulk carriers, cargo ships, and passenger vessels, and also within certain geographic areas, including the Northwest Passage. The combination of climate change – induced increases in sea ice – related navigational hazards and the observed decrease in highly strengthened ships operating in the Canadian Arctic could lead to a larger number of accidents and incidents as a proportion of total operational vessels, and points to the need for infrastructure and service investment congruent with overall increases in particular types of maritime shipping activities expected in the near- to medium-term future.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".