A decade of monitoring cruise ship tourism in the Canadian Arctic: An overview of key trends
Bibliographic record
Abstract
This poster describes key patterns of cruise ship tourism activity across the Canadian Arctic from 2006‐2015. Cruise ships have been visiting the region since 1984, but determining the actual number of cruise ships, the destinations visited and routes taken is problematic. Arctic Canada’s vessel monitoring service of the Canadian Coast Guard is mandated to collect positioning data only for vessels above 300 gross tonnes, and Parks Canada collects a limited amount of information on northern park visitors. In order to address this data gap we have been collecting cruise data from internet sites since 2006. The process involves a systematic review annually of operator websites and builds a database of planned cruises taking particular note of the routes to be taken and the sites the cruise ships intend to visit. In this poster we review and explain patterns of activity. One pattern illustrates change in numbers of itineraries and shows growing numbers to 2010, followed by a brief decline, and now a return to growth. Another pattern reflects spatial and regional changes in itineraries that have seen vessel traffic concentrated further north (i.e. along the Northwest Passage) and east (i.e. Baffin Bay) than previously. The explanation for these patterns relates to demand, economic conditions, vessel compliance, legislative frameworks, and climate change. To our knowledge, this research provides a unique data set on cruise activity in Arctic Canada over the last decade.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.062 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".