Impact of climate change scenarios on the agroclimate of the Canadian prairies
Bibliographic record
Abstract
Regional climate change scenarios for the Canadian prairies were generated using historic weather data and daily data from two Canadian Climate Centre general circulation models (GCM). Model CGCM1-A was a recent version release while its predecessor was model GCMII. The GCM data were combined with historic values to generate two additional scenarios. All scenarios were used to drive the modified Versatile Soil Moisture Budget model that assessed soil moisture, aridity and other agroclimatic indices. The modelled results for all scenarios were compared to those using the historic climate data. The model predicted earlier seeding dates for spring wheat between 18 and 26 d. Early seeding and harvest was shown to be an appropriate adaptive strategy that avoided more arid conditions in the late summer. The soil water deficit was lower under GCMII than historic values by 46 mm. For CGCM1-A, the soil water deficit was decreased by 8 mm across the Prairie Provinces compared to historic values. GCM scenarios predicted unchanged or increased soil water in the top 120 cm soil across the Canadian prairies compared to the historic scenario. There were some regions such as south eastern Saskatchewan and southern Manitoba where reductions in summer rainfall (for CGCM1-A) were large. These regions experienced the greatest benefit of earlier seeding dates. Key words: Climate change, agriculture, aridity, growing degree-days, soil moisture, seeding date, harvest date
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".