Comparative and critical analysis of data sources used for ship traffic spatial pattern analysis in Canada and across the global Arctic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
• First comparative analysis of NORDREG, S-AIS and ASTD datasets for Arctic shipping. • Fusing S-AIS and ASTD enhances vessel detection across Arctic maritime regions. • S-AIS outperforms ASTD in Northwest Passage and Northern Sea Route coverage. • ASTD excels in regions with dense terrestrial AIS, like Norway and Iceland. • Arctic vessel detections show consistent growth from 2011 to 2022. This study presents a comprehensive comparative analysis of three primary datasets commonly employed to evaluate shipping patterns in Arctic waters: 1) Northern Canada Vessel Traffic Zone (NORDREG), 2) satellite-based Automatic Identification System (S-AIS) from a private provider, and 3) the Arctic Ship Traffic Database (ASTD). Covering the years 2011 to 2022, the analysis assesses spatial and temporal metrics for each dataset while employing robust data cleaning techniques to address signal manipulation and detection gaps. Findings reveal that S-AIS and NORDREG excel in detecting vessel traffic in Canadian waters, including the Northwest Passage (NWP), while ASTD demonstrates strong performance in regions with dense terrestrial AIS coverage, such as Norway and Iceland. However, ASTD is less effective along critical shipping routes, including the NWP and the Northern Sea Route (NSR), where S-AIS provides broader coverage. Both datasets indicate an upward trend in AIS-based traffic throughout the Arctic. The results underscore the value of fusing S-AIS and ASTD datasets to provide a more complete and accurate understanding of Arctic shipping patterns. This research offers critical insights for policymakers and researchers selecting ship traffic data for regional and global Arctic analyses, maritime safety, and environmental decision-making.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it