Assessment of climate change on the Canadian prairies from downscaled GCM data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it