Examining the Viability of the World’s Busiest Winter Road to Climate Change Using a Process-Based Lake Model
Bibliographic record
Abstract
Abstract Winter roads play a vital role in linking communities and building economies in the northern high latitudes. With these regions warming 2–3 times faster than the global average, climate change threatens the long-term viability of these important seasonal transport routes. We examine how climate change will impact the world’s busiest heavy-haul winter road—the Tibbitt to Contwoyto Winter Road (TCWR) in northern Canada. The FLake freshwater lake model is used to project ice thickness for a lake at the start of the TCWR—first using observational climate data, and second using modeled future climate scenarios corresponding to varying rates of warming ranging from 1.5° to 4°C above preindustrial temperatures. Our results suggest that 2°C warming could be a tipping point for the viability of the TCWR, requiring at best costly adaptation and at worst alternative forms of transportation. Containing warming to the more ambitious temperature target of 1.5°C pledged at the 2016 Paris Agreement may be the only way to keep the TCWR viable—albeit with a shortened annual operational season relative to present. More widely, we show that higher regional winter warming across much of the rest of Arctic North America threatens the long-term viability of winter roads at a continental scale. This underlines the importance of continued global efforts to curb greenhouse gas emissions to avoid many long-term and irreversible impacts of climate change.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".