Projected changes in the hotspots for agriculturally relevant compound events in Western Canada cropping regions under the <scp>RCP8</scp>.5 scenario
Bibliographic record
Abstract
Abstract The Canadian Prairies are a major grain production region, producing most of the wheat for export in Canada. Global warming and the associated changes in extreme precipitation and temperature events pose significant risks to agriculture on the Canadian Prairies. Compound hazards can cause higher crop failure than isolated events, especially in the main grain production regions in western Canada. To achieve informed climate risk management, it is critical to characterize the threats posed by compound hazards in current and future climates in western Canada. In this study, return periods of events were computed to assess the potential changes in the hotspots for agriculturally relevant compound events in western Canada using two convection‐permitting climate simulations: current (CTL) climate and future climate under the RCP8.5 scenario based on a pseudo‐global‐warming (PGW) approach. Specifically, our study analyzed agricultural drought, low precipitation, heatwaves, and cool waves related to cool‐season crops. The results showed the overall good performance of the CTL simulation in capturing spatial patterns of these compound events in western Canada. In the current climate, droughts and heatwaves co‐occur mostly in southeastern parts of the prairies. Under the RCP8.5 scenario, they are likely to increase in frequency and expand to cover the major croplands of western Canada. This study provides information that policymakers in the fields of climate change adaptation and agricultural disaster management will find useful.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".