Summary and synthesis of Changing Cold Regions Network (CCRN) research in the interior of western Canada – Part 1: Projected climate and meteorology
Bibliographic record
Abstract
The interior of western Canada, up to and including the Arctic, has experienced rapid change in its climate, hydrology, cryosphere, and ecosystems, and this is expected to continue. Although there is general consensus that warming will occur in the future, many critical issues remain. In this first of two articles, attention is placed on atmospheric-related issues that range from large scales down to individual precipitation events. Each of these is considered in terms of expected change organized by season and utilizing mainly “business-as-usual” climate scenario information. Large-scale atmospheric circulations affecting this region are projected to shift differently in each season, with conditions that are conducive to the development of hydroclimate extremes in the domain becoming substantially more intense and frequent after the mid-century. When coupled with warming temperatures, changes in the large-scale atmospheric drivers lead to enhancements of numerous water-related and temperature-related extremes. These include winter snowstorms, freezing rain, drought, forest fires, as well as atmospheric forcing of spring floods, although not necessarily summer convection. Collective insights of these atmospheric findings are summarized in a consistent, connected physical framework.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.031 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".