Genetic and phenotypic analyses indicate that resistance to flooding stress is uncoupled from performance in cultivated sunflower
Bibliographic record
Abstract
Given the rising risk of extreme weather caused by climate change, enhancement of abiotic stress resistance in crops is increasingly urgent. But will the development of stress-resistant cultivars come at the cost of yield under ideal conditions? We hypothesize that this need not be inevitable, because resistance alleles with minimal pleiotropic costs may evade artificial selection and be retained in crop germplasm. Genome-wide association (GWA) analyses for variation in plant performance and flooding response were conducted in cultivated sunflower, a globally important oilseed. We observed broad variation in flooding responses among genotypes. Flooding resistance was not strongly correlated with performance in control conditions, suggesting no inherent trade-offs. Consistent with this finding, we identified a subset of loci conferring flooding resistance, but lacking antagonistic effects on growth. Genetic diversity loss at candidate genes underlying these loci was significantly less than for other resistance genes during cultivated sunflower evolution. Despite bottlenecks associated with domestication and improvement, low-cost resistance alleles remain within the cultivated sunflower gene pool. Thus, development of cultivars that are both flooding-tolerant and highly productive should be straightforward. Results further indicate that estimates of pleiotropic costs from GWA analyses explain, in part, patterns of diversity loss in crop genomes.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".