Estimating Ship Transit Times in Ice-Covered Waters for Strategic Route Analysis and Search and Rescue Response Planning
Bibliographic record
Abstract
Maritime traffic in the Arctic region is increasing as northern communities grow, tourism accelerates, and large resource development projects enter operation. Consequently, the number of vessels exposed to the navigational challenges and risks in the polar region will continue to rise. The situation is further complicated by fast-changing sea ice conditions due in part to climate change. This thesis explores the state of the art in sea ice risk assessment and transit time estimation in ice-covered waters, and presents a strategic route planning methodology that integrates several concepts from these two active areas of research. This methodology is used to enable the computation of innovative visual representations of marine-based search and rescue response time throughout the year in the Canadian Arctic. This is achieved by combining statistical methods, advanced geospatial data analysis, and network analysis techniques to overcome several computational challenges specific to route generation and transit time estimation in ice-covered waters. The results indicate that there is a statistical relationship between reported vessel speed from Automatic Information System (AIS) and the operational risk from sea ice determined at the time of reporting using the Polar Operational Limit Assessment Risk Indexing System (POLARIS). This relationship is used to specify the expected ship speed in different sea ice risk categories, which is then used to compute the fastest route and expected transit time between geographically separated locations in ice-covered waters. The model can generate the fastest route at different times of year and for different ship ice classes. This is achieved by exploiting the relationship that exists between vessel speed and the outcome of the POLARIS assessment. The methods and results presented in this thesis are shown to support a variety of strategic route analysis applications and provide the necessary computational toolset to apply advanced area-based management approaches to maritime Search and Rescue response planning.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".