An evaluation of the Canadian Regional Climate Model simulation of the 1999 to 2004 drought over the Canadian Prairies
Bibliographic record
Abstract
The information from the Canadian Regional Climate Model (CRCM) can be applied to improve prediction of Prairie drought in order to reduce its devastating environmental, societal, and economical effects. One can, for example, use the CRCM to investigate the importance of certain feedbacks in maintaining the drought. A necessary step before using the CRCM for such purposes is to establish how well the model reproduces observed features of the drought. In this study, satellite and surface station data from the recent and severe Canadian Prairie drought of 1999-2004 are used to compare with the model output. The absolute data fields examined include precipitation, cloud properties, and top-of-atmosphere albedo. Cloud amount-Standardized Precipitation Index (SPI) correlations, and top-of-atmosphere albedo-SPI correlations are also compared. Overall, the CRCM performs well in the areas examined and gives confidence in its usefulness as a tool to understanding Prairie drought.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".