Northern marine transportation corridors: Creation and analysis of northern marine traffic routes in Canadian waters
Bibliographic record
Abstract
Abstract The concept of the Northern Marine Transportation Corridors (NMTC) initiative was developed under the Government of Canada World‐Class Tanker Safety System Initiative (WCTSS). The NMTC is an interdepartmental Arctic initiative within the Department of Fisheries and Oceans Canada (DFO), through the Canadian Coast Guard (CCG) and the Canadian Hydrographic Service (CHS), in collaboration with Transport Canada (TC). The NMTC initiative was developed to strengthen the safety of marine navigation in the Arctic, and to offer an efficient planning guide for present and future Arctic investments. Transportation corridors identified through this initiative will provide the Government of Canada the framework needed to better prioritize and deliver on its programs and services, including: nautical charts and products; aids to navigation; icebreaking services; and marine safety regulations. The corridors were generated and analyzed with a Geographic Information System (GIS) using two main data sources: the Automated Identification System (AIS) and CHS's nautical charts and publications. The geographic extent of the NMTC is defined as the Northern Canada Vessel Traffic Service Zone (NORDREG Zone) and the Mackenzie River. With close to 4 million km 2 of water in the Arctic and 162,000 km of coastline, surveying the Arctic to modern standards represents an enormous challenge to CHS. By adopting a corridor‐based approach, CHS and other government programs can prioritize their efforts on 12% of the Canadian Arctic waters. CHS currently has 32% of the NMTC adequately surveyed, with an additional 3% surveyed to modern standards.
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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 | 0.009 | 0.015 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".