Climate Change Adaptation Assessment for Transportation in Arctic Waters (CATAW) Scoping Study, Summary Report
Bibliographic record
Abstract
This scoping study report presents the preliminary analysis of: 1) changing shipping movements in the Canadian Arctic from 1990 to 2011 by total shipping volume and vessel type, and 2) the relationship between changing shipping patterns and sea ice reduction and variability. There has been a significant increase in shipping volume over the past decade. Overall vessel counts increased by 40% from 2006 to 2007 and by 20% from 2007 to 2011. Accounting for annual variability, total vessel volume has increased by more than 75% over the past decade. The most dramatic increase in marine activity involves the rapidly evolving pleasure craft industry (e.g., small vessel recreational boating), which is expected to continue to increase in the near future. The traffic categories of passenger vessels, government vessels and icebreakers, and bulk carriers are also on the rise. The shipping season is getting longer. Combined monthly vessel count trends for all vessels show statistically significant increases in travel during the shoulder season months of June and November. The shipping season is beginning earlier for some vessel types (e.g., Fishing Vessels, Tanker Ships) and extending later into November for other vessel types (e.g., General Cargo). During the shipping season (June 25 to October 15) sea ice in the NORDREG zone experienced declines of total ice, multi-year ice and first year ice over the period 1990–2011 that are statistically significant. Decreasing multi-year ice combined with increased prevalence of younger and thinner first year ice can increase ease of navigation. The greatest reductions in multi-year ice occurred in September through November. Shipping activity has increased in a stepwise manner coincident with the 2007 extreme ice minima, which has persisted in all summers since.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".