Contrasting trends in regional arctic destinational and transit shipping
Bibliographic record
Abstract
Ever since the impact of climate change on Arctic sea ice began to be discussed in \ninternational forums at the turn of the century, several comments were published to the \neffect that diminishing sea ice would quickly translate into the development of massive \ntransit routes across the Northwest Passage (NWP), the Northern Sea Route (NSR) and the \nArctic Bridge linking Churchill on the shores of Hudson Bay and Murmansk. Twenty years \nlater, Arctic shipping did indeed expand significantly, but the actual picture is significantly \ndifferent from what analysts projected. Destinational traffic appears to be the driver of \nArctic shipping expansion, while transit traffic remains marginal. What are the main \nfeatures of Arctic shipping presently, and how did the industry adapt, depending on the \narea? Results show contrasting evolutions along the NSR, in the Canadian Arctic, and in \nGreenlandic waters. \nThis chapter is based on the analysis of figures from three different sources, which \nimplies methodological issues since the data does not display the same elements (Lasserre \nand Alexeeva, 2015; Lasserre 2019). In the Russian Arctic, data about vessel movements \nand characteristics were gathered from the Northern Sea Route Administration1 and from \nthe Center for High North Logistics. For the Canadian Arctic, the Ministry of \nTransportation agency for the Northern Canada Vessel Traffic Services Zone Regulations \nprovided the author with annual detailed ship movements. For Greenlandic waters, data \nwas provided by the Danish Joint Arctic Command based in Nuuk
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".