Arctic Shipping in Canada: Analysis of Sea Ice, Shipping, and Vessel Track Reconstruction
Bibliographic record
Abstract
Declining sea ice area in the Canadian Arctic has gained significant attention with respect to the prospect of increased shipping activities along the Northwest Passage and Arctic Bridge shipping routes. Temporal trend and correlation analysis was performed on sea ice area data for total, first-year ice (FYI), and multi-year ice (MYI), and observed shipping activity within the Vessel Traffic Reporting Arctic Canada Traffic Zone (NORDREG zone) from 1990 to 2012. Relationships between declines in sea ice area and Arctic maritime activity were investigated alongside linkages to warming surface air temperatures (SAT) and an increasing melt season length. Statistically significant increases in vessel traffic were observed on monthly and annual time-scales, coincident with declines in sea ice area. Despite increasing trends, only weak correlations between the variables were identified, suggesting that other non-environmental factors have likely contributed to the observed increase in Arctic shipping activity including tourism demand, community re-supply needs, and resource exploration trends. As a first step towards quantifying spatial variability in shipping patterns, a case study was conducted using 2010 observed shipping data to reconstruct historical shipping routes using a least cost path (LCP) approach. This approach was able to successfully reconstruct vessel tracks compared to an independent data source (Automatic Identification System) to an accuracy of 10.42 km ± 0.67 km over the entire study area. A 25 km gridded product across the entire Canadian Arctic domain was produced for 2010, with this approach now providing a basis to apply this method over the entire record (since 1990) in future studies to investigate long term spatial variability and change of shipping activity across the Canadian Arctic.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".