Amplified agricultural impacts from more frequent and intense sequential heat events
Bibliographic record
Abstract
Abstract As the climate warms, interacting weather extremes such as sequential heat events pose complex risks to societies. Regarding global agriculture, laboratory experiments suggest that early crop exposure to heat may either confer tolerance or enhance vulnerability to subsequent heat during the critical crop flowering stage. We show that warm early-seasons improve crop yield potential, particularly for soybean and maize, but also increase the impacts of subsequent heat by 5%–55% compared to years with average early-season temperatures. The impacts of this increased yield sensitivity outweigh the benefits of early season heat when mid-season temperature anomalies exceed 0.7 ∘ C–5 ∘ C (depending on the crop). Analyzing projected temperatures under the Shared Socioeconomic Pathway 3-7.0, we find a tenfold increase in the likelihood of experiencing sequential heat in early and mid-season crop growth stages, defined as a joint 90th percentile event. Accounting for the interactive effects of early and mid-season warming increases projected temperature-related crop yield losses by 2%–44%, depending on crop and region. These results underline the emerging nonlinear risks from sequential heat extremes to food systems, which can largely be avoided when limiting warming to 1.5 ∘ C globally.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".