Silver lining to a climate crisis: multiple prospects for alleviating crop waterlogging under future climates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Extreme weather events threaten food security, yet global assessments of crop waterlogging are rare. Here, we make three important contributions to the literature. First, we develop a paradigm that distils common stress patterns across environments, genotypes and climate horizons. Second, we embed improved process-based understanding into a contemporary farming systems model to discern changes in global crop waterlogging under future climates. Third, we elicit viable systems adaptations to waterlogging. Using projections from 27 global circulation models, we show that yield penalties caused by waterlogging increased from 3–11% historically to 10–20% by 2080. Altering sowing time and adopting waterlogging tolerant genotypes reduced yield penalties by up to 18%, while earlier sowing of winter genotypes alleviated waterlogging risk by 8%. We show that future stress patterns caused by waterlogging are likely to be similar to those occurring historically, suggesting that adaptations for future climates could be successfully designed using current stress patterns.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it