Temporal and Spatial Patterns of Ship Traffic in the Canadian Arctic from 1990 to 2015 + Supplementary Appendix 1: Figs. S1–S7 (See Article Tools)
Bibliographic record
Abstract
The limited availability of consistent, longitudinal data sources for marine traffic in Arctic Canada has presented significant challenges for researchers, policy makers, and planners. Temporally and spatially accurate shipping data that reveal historical and current traffic trends are vital to plan safe shipping corridors, develop infrastructure, plan and manage protected areas, and understand the potential environmental and cultural impacts of change, as well as for sovereignty and safety considerations. This study uses a recently developed geospatial database of ship traffic to provide the first synthesized overview of the spatial and temporal variability of different vessel types in Arctic Canada during the 26-year period from 1990 to 2015. This examination shows that, overall, the distance traveled by ships in Arctic Canada nearly tripled (from 364 179 km in 1990 to 918 266 km in 2015), that the largest proportion of ship traffic in the region is from general cargo vessels and government icebreakers (including research ships), and that the fastest growing vessel type by far is pleasure craft (private yachts). Spatial shifts in vessel activity over the last quarter century have favoured areas with active mine sites, as well as the southern route of the Northwest Passage. As a result, some communities, including Baker Lake, Chesterfield Inlet, Pond Inlet, and Cambridge Bay, are experiencing greater increases in ship traffic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.002 | 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) | 0.011 | 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".