Warmer temperatures provide little benefit in offsetting cold stress in Canadian crop yields
Bibliographic record
Abstract
Abstract The net effect of warmer temperatures for cold-climate agriculture remains unknown: cold temperatures disrupt production, but warmer temperatures can also bring yield-reducing extreme heat. We introduce an approach to measure cold-temperature exposure using sinusoidal degree days, an exact analog to the approach widely used to measure heat exposure. Applying this approach to model the yields of six Canadian crops, we find yield penalties to cold-temperature exposure mirror those from extreme heat. While average yields under low- and high-emission scenarios increase significantly when warmer days mitigate cold damage, these gains are too small to offset additional extreme-heat damage, and the net effect remains broadly negative. For barley, canola, oats and wheat, damages are severely negative (−28.4% to −57.8% loss). For maize and soybean, outcomes range from no distinguishable change to moderate losses (−5.2%). Additionally, yield risk increases, with higher coefficients of variation and low-yield probabilities across all scenarios.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".