Arctic roads and railways: social and environmental consequences of transport infrastructure in the circumpolar North
Bibliographic record
Abstract
Land-based transport corridors and related infrastructure are increasingly extending into and across the Arctic in support of resource development and population growth, causing large-scale cumulative changes to northern socio-ecological systems. These changes include the increased mobility of people, goods and resources, and environmental impacts on landscapes and ecosystems as the human footprint reaches remote, unindustrialized regions. Arctic climate change is also generating new challenges for the construction and maintenance of these transport systems, requiring adaptive engineering solutions as well as community resilience. In this review article, we consider the complex entanglements between humans, the environment, and land transportation infrastructure in the North and illustrate these interrelations by way of seven case studies: the Baikal–Amur Mainline, Bovanenkovo Railway, Alaska–Canada Highway, Inuvik–Tuktoyatuk Highway, Alaska Railroad, Hudson Bay Railway, and proposed railways on Baffin Island, Canada. As new infrastructure is built and anticipated across the circumpolar North, there is an urgent need for an integrated socio-ecological approach to impact assessment. This would include full consideration of Indigenous knowledge and concerns, collaboration with local communities and user groups in assessment, planning and monitoring, and evaluation of alternative engineering designs to contend with the impacts of climate change in the decades ahead.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".