Adaptive introgression from local cultivars is a driver of agricultural adaptation in Argentinian weedy rice
Bibliographic record
Abstract
Weedy rice, a pervasive and troublesome weed found across the globe, has often evolved through feralization of rice cultivars. In the South American Southern Cone region, rice breeding has a contemporary history and weedy rice has been reported as an important constraint since early 1970s; however the origin and genetic composition of Argentinian weedy rice has not been explored so far. To study this, we used genotyping-by-sequencing to generate genome-wide single nucleotide polymorphisms (SNPs) and compared Argentinian weedy rice with whole-genome data from strains, rice cultivars and wild rice accessions from regions worldwide. In addition, we conducted a comprehensive seed and phenological characterization, and performed an herbicide resistance screening, taking into account mutations in the ALS gene observed in the Argentinian weedy rice collection. Our results revealed large variability in seed traits and flowering time in Argentinian weedy rice. Additionally, most strains were resistant to ALS-inhibiting herbicides with a high frequency of the ALS mutation (A122T), which is also present in Argentinian rice cultivars. Cultivated rice varieties in Argentina belonged to the three major groups: japonica, indica and aus. In contrast, Argentinian weedy rice strains were mostly aus and aus-indica hybrids, resembling cultivars and weedy rice strains from the Southern Cone region. Our findings suggest that Argentinian weedy rice descends from aus weeds, presumably from the United States, which contemporaneously hybridized with local indica cultivars. The observed genetic similarities within the Southern Cone region can be attributed to the continuous exchange of breeding germplasm and cultivars, which, in turn, facilitated the dissemination of weedy rice strains.
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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 | 0.001 | 0.001 |
| 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".