Resilience and adaptation of First Nations communities in Canada to disappearing winter road infrastructure in a changing climate
Bibliographic record
Abstract
In Canada’s North, winter roads serve as vital lifelines for remote First Nations communities, connecting them to essential resources and services. Constructed over seasonally frozen lakes, rivers, and land, these temporary roads are the only means to transport food, fuel and building materials in large volumes. Winter temperature increases of > 3° C in several provinces have already led to shorter operating seasons and less lake ice thickness, compromising safety, supply, and well-being.Limited meteorological data, a lack of economic or political relevance and the provincial jurisdiction over winter roads have so far discouraged broader research. The few localised studies leave a large knowledge gap with respect to the historical correlation of climate data with winter road seasons and the ability to predict their future. In addition, scientific studies rarely include the existing traditional environmental knowledge without which the adaptive capacity and resilience potential of Indigenous communities cannot be fully understood and realised.Using GIS tools to create a map of all Canadian winter road systems, ERA5 data to analyse location-specific temperature trends, and observations of lake ice thickness to validate a one-dimensional lake model as proxy for freezing trends all aim to explore the natural science base. Surveys and extended interviews in a Manitoba First Nations community complement the study in a decolonising approach, following the concept of Two-Eyed Seeing.Comprehensive mapping shows that most winter road tracks have recently been rerouted to avoid lake surfaces despite the difficult terrestrial underground. Temperature trends are highest in January and vary from +0.4° C in Ontario to +1° C per decade in the Northwest Territories, while modelled ice thickness has decreased between 9% and 14% from 1950 until 2022. Shorter winter road seasons have resulted in food insecurity, educational deprivation, and a housing crisis in many remote First Nations communities, worsened by the intergenerational trauma of residential schools and legislative hurdles to self-determination as defined by the UN Declaration on the Rights of Indigenous Peoples.For Indigenous communities in Canada, cryosphere services are not limited to winter road infrastructure, they include traditional food harvesting, cultural connectivity and identity. Without a profound connection to the land, change observations remain inconsequential, adaptive measures and resilience unobtainable.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".