Neo-Domestication of an Interspecific Tetraploid Helianthus annuus × Helianthus tuberous Population That Segregates for Perennial Habit
Bibliographic record
Abstract
Perennial agriculture has been proposed as an option to improve the sustainability of cropping systems, by increasing the efficiency of resource use, while also providing ecosystem services. Neo-domestication, the contemporary domestication of plants that have not previously been used in agriculture, can be used to generate new crops for these systems. Here we explore the potential of a tetraploid (2n = 4x = 68) interspecific hybrid sunflower as a perennial oilseed for use in multifunctional agricultural systems. A population of this novel tetraploid was obtained from crosses between the annual diploid oilseed crop Helianthus annuus (2n = 2x = 34) and the perennial hexaploid tuber crop Helianthus tuberosus (2n = 6x = 102). We selected for classic domestication syndrome traits for three generations. Substantial phenotypic gains were made, in some cases approaching 320%. We also analyzed the genetic basis of tuber production (i.e., perenniality), with the goal of obtaining molecular markers that could be used to facilitate future breeding in this system. Results from quantitative trait locus (QTL) mapping suggest that tuber production has an oligogenic genetic basis. Overall, this study indicates that substantial gains towards domestication goals can be achieved over contemporary time scales.
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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".