Climate Change and Biofuel Wheat Production in Southern Saskatchewan: Long-Term Climate Trends Versus Climate Modeling Predictions
Bibliographic record
Abstract
Climate modeling work has suggested biofuel wheat production in southern Saskatchewan, Canada, during the mid-21st century will be influenced by increasing annual precipitation, including precipitation increases in every month except July and August, increasing daily mean, minimum, and maximum air temperatures throughout the year, and substantial increases in the risk of wheat heat shock (temperatures>32.0 C). In the current study, we compare prior modeling predictions to historical trends in the number of days with maximum temperatures >32.0 C during July and August, the number of hours with maximum temperatures >32.0 C during July, as well as monthly and annual total precipitation, mean daily temperatures, and mean maximum daily temperatures for climate stations throughout southern Saskatchewan. We find no evidence of increasing trends for wheat heat shock days or hours during the mid-summer period in this region. In contrast, the majority of stations exhibit significantly declining temporal trends in wheat heat shock days and hours. Historical precipitation and temperature trends for the climate stations under consideration in southern Saskatchewan display significant inter- and intra-station heterogeneity throughout the year in terms of whether or not trends are evident, as well as their magnitude and direction. Consequently, caution must be exercised when extrapolating any case study analyses at a particular location to larger geographic areas of the province. Based on our analyses of historical climate data for southern Saskatchewan, it is unclear whether climate models are accurately predicting future climate change impacts on biofuel wheat production for this region in the mid-21st century.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".