Climate change and permafrost thaw-induced boreal forest loss in northwestern Canada
Bibliographic record
Abstract
Permafrost distribution throughout the western Canadian subarctic is not well understood due to the remoteness and size of the region, its spatial and temporal heterogeneity, limited data availability, and sparse monitoring networks. These factors severely challenge investigations of how climate warming might affect the distribution of permafrost and provide strong justification for new methods of evaluating permafrost extent using remote sensing platforms. This study quantifies forest loss at ten subarctic boreal sites in the southern Northwest Territories and northeastern British Columbia between 1970 and 2010. Historical air photos and optical remote sensing images were assessed using a change detection approach over the ten sites, each 10 km 2 spanning a north/south transect of 200 km. This study is the first to apply change detection methods to a large-scale gradient and spans the southern margin of discontinuous permafrost where results demonstrate variable patterns of net forest loss at each site ranged from 6.9% to 11.6% over the 40 year study period. Here we show that these differential rates of landcover change can be explained in part through climatic and environmental factors that vary latitudinally across the selected sites. Change statistics—net change, forest gain and forest loss were significantly correlated with an assortment of factors that varied across the ten-site transect.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".