Modelled changes in selected agroclimatic indices over the croplands of western <scp>Canada</scp> under the RCP8.5 scenario
Bibliographic record
Abstract
Abstract To assess the potential change in agroclimatic indices in western Canada, this study used a convection‐permitting Weather Research Forecasting (WRF) model to conduct simulations for the current climate (CTL, 2000–2015) and future climate under the RCP8.5 scenario based on a pseudo‐global‐warming (PGW) approach. Both CTL and PGW simulations were bias‐corrected to the GEM‐CaPA dataset using a multivariate quantile mapping method. An evaluation of the CTL simulation of daily maximum and minimum temperatures and precipitation during the growing season against the gridded observations has been performed, indicating good agreements in the spatial patterns of air temperature and precipitation in western Canada. The PGW − CTL differences in several selected agroclimatic indices were then examined. Due to rising temperatures, substantial increases in growing degree‐days (GDD) by 800–1,200° days and reductions in frost days by 10 to 20 days, favouring regional crop production, are found in southern Alberta and Saskatchewan. However, global warming also poses great risks to Canadian agriculture by modifying heat accumulations and water availability during the growing season. Plant heat stress will substantially increase by ∼50° days in southern Alberta and Saskatchewan, offsetting the positive effects caused by the reduction in frost days and increase in GDD. The southern Canadian Prairies will experience statistically significant increases in the number of dry days and precipitation deficit, suggesting an exacerbation of water stress on the Canadian Prairies by global warming.
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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 | 0.001 | 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".