Climate Change and Arctic Coastal Infrastructure and Transportation
Bibliographic record
Abstract
There are increased concerns related to the impact of a possible climate change on Arctic coastal infrastructure, transportation and exploitation of natural resources. The average global surface temperature is projected to increase from 1.4 to 5.8°C between 1990 and 2100. Output from general circulation models (GCMs), show that the environmental conditions of the Arctic coast will change drastically with an increase in global surface temperature. The aerial extent and thickness of ice will change, in general showing lesser ice extent and thickness. Thinner, less extensive sea ice will generally improve navigation conditions along most northern shipping routes, such as the Northwest Passage, offshore of Canada, the Northern Sea Route and offshore of Russia. Lesser ice extent and thickness will probably provide an opportunity for export of natural resources and other waterborne commerce over new northern shipping routes. However, more open water allows wave generation by winds over longer fetches and durations. Wave-induced coastal erosion along Arctic shores is expected to increase with global warming. Sea level rise is also an effect of increased surface and seawater temperatures that has to be taken into consideration in coastal infrastructure design. Permafrost coasts are especially vulnerable to erosive processes as ice beneath the seabed and shoreline melts from contact with warmer air and water. Low-lying ice-rich Arctic permafrost coasts are most vulnerable to thaw subsidence and subsequent wave-induced erosion. Global warming may also change Arctic rivers as transportation routes, water sources, and habitats. This paper discusses results from general circulation models and the impact of predicted climate scenarios on ice conditions, coastal environment and transportation.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".