Future canola yields under different climate scenarios in Saskatchewan, Canada
Bibliographic record
Abstract
Canola is the largest crop in Saskatchewan and critically important to the province's agricultural industry. Yields are sensitive to high temperatures and likely to be impacted by future climate change. To estimate the future effects of climate change (2025–2100) on canola yields, data from 26 climate models for three future emissions pathways (shared socioeconomic pathway (SSP) 1–2.6 (low), 2–4.5 (medium), and 5–8.5 (high)) along with predictive soil mapping results was used to build a predictive model. Historical yield data was modelled as a function of soil organic carbon, clay, and sand along with maximum and minimum July temperature, days with maximum temperatures above 30 °C, nights with minimum temperatures above 16 °C, and mean total annual precipitation. Modelled outcomes indicate that yields were more sensitive to temperature than precipitation and decline when mean daily July maximum temperatures were above 28.5 °C. Under SSP1-2.6, canola yields remain relatively stable with a median decline of 39 kg ha −1 (2%). Under SSP2-4.5 and SSP5-8.5, canola yields were estimated to decline by 137 kg ha −1 (6%) and 349 kg ha −1 (15%), respectively. The distribution of yield losses was not equal with the greatest decreases occurring in the Black soil zone.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".