Assessment of climate change on the Canadian prairies from downscaled GCM data
Bibliographic record
Abstract
Climate data were taken from the Canadian Centre for Climate Modelling and Analysis (CCCma) Second Generation Global Circulation Model (GCMII) and the more recently developed Canadian Coupled Global Circulation Model with aerosol (CGCM1‐A). The GCM output difference for a current and doubling CO2 concentration was used to modify a 30‐year historic time series. Regional climate change data under a doubling of CO2 were produced by downscaling to a grid of 50 by 50 km across Alberta, Saskatchewan and Manitoba. Two scenarios were produced containing GCM‐generated temperatures and precipitation. Results show that, across all three provinces, maximum air temperature is predicted to have a mean increase of 4.0° to 5.7°C (GCMII) and 2.5° to 3.3°C (CGCM1‐A) above climate normal values. Minimum air temperature is expected to have a mean increase of 5.0° to 5.6°C (GCMII) and 3.0° to 3.3°C (CGCM1‐A). Precipitation is predicted to have a mean increase of 29 to 36% (GCMII) and 3 to 7% (CGCM1‐A). Both the GCMII and CGCM1‐A indicate that central Alberta will benefit the most during the summer and winter from increased precipitation, the eastern Prairies, however, will see little change (winter) in precipitation with smaller increases (30 mm under GCMII) or a decrease (30 mm under CGCM1‐A). Overall, the CGCM1‐A results are more consistent than GCMII with historic large‐scale spatial patterns.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".