Recurrent Selection for Improved Germination under Water Stress in Russian Dandelion
Bibliographic record
Abstract
Russian dandelion [ Taraxacum kok-saghyz (TKS)] is a promising candidate for introducing natural rubber production into North America. Seeds normally germinate in a humid microenvironment, such as the thatch layer of a lawn or under a canopy of grass; however, 5% to 15% establishment is often observed on bare soil, presumably due to water stress. Phenotypic selection and half-sib family recurrent selection were conducted for three cycles to improve germination in vitro, under low osmotic potential (Ψ s ), using a polyethylene glycol (PEG) solution. Populations were then tested for establishment on bare soil in the greenhouse and field. Germination under water stress in vitro increased from 5.8% for the cycle 0 (C 0 ) population to 40.8% and 47.8% for the C 3 -phenotypic and C 3 -half-sib family populations, respectively. Soil establishment in the greenhouse and field was improved up to two- and 4-fold, respectively, compared with the C 0 , in two of four greenhouse experiments and three of eight field experiments. Overall, recurrent selection for germination under water stress in vitro has potential to improve establishment in the field and can be incorporated into current breeding programs to support the overall goal of creating cultivars with high-rubber yield.
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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".