The Future of Northern Canadian Land Use in the Age of Climate Change
Bibliographic record
Abstract
Climate change and land use alterations are interdependent and change in one causes a change in the other. Climate change is projected to expand agricultural lands especially at higher latitudes like northern regions of Canada. However, the spatiotemporal extent of this land use change is not clear and will be affected by multiple factors. This research provides a descriptive modelling and analysis of climate change-driven agricultural expansions (CCDAEs) in northern Canada. We discuss the consequences of CCDAE progress in Canada’s North in terms of climate change-driven soil loss, greenhouse gas (GHG) emissions, and associated environmental impacts. Results revealed that just over 135 million hectares of northern Canada’s lands could change to agricultural lands through different CCDAE scenarios in four timeframes between 2025–2100. The scenarios were categorized to address Indigenous sovereignty on their treaty lands and sustainability of peatlands and mountain areas along with the most likely CCDAE patterns. The CCDAE is projected to cause 29− 185 × 103 megaton (MT) soil loss, and 1.7− 8.6 × 105 MT carbon dioxide equivalent GHG emissions in minimal/maximal situations. This huge CCDAEs in Canada’s north will have considerable footprints on the environment, local communities, climate change mitigation plans, global food security, and local/national economic opportunities. Data and analyses can be used by provincial/territorial governments, policymakers, and environmental planners for future land use changes planning and infrastructure and rural development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".