Phenotypic and Genetic Uniformity in Three Populations of <i>Panax notoginseng</i> by Mass Selection
Bibliographic record
Abstract
Aim: Panax notoginseng is an important traditional Chinese medicinal plant. Although the species has been cultivated for more than 400 years, a certified variety is still not available. Natural populations (NP) exhibit high levels of morphological variation and genetic diversity, suggesting that mass selection may be used to improve P. notoginseng. In previous studies, we established 38 mass selection populations based on certain morphological traits (target characters) and eliminated undesirable individuals over five successive generations. The objective of the present study was to evaluate phenotypic and genetic uniformity of three of these populations: purple-stem (PSP), green-stem (GSP), and erect-type (ETP) populations. Methods: To assess phenotypic uniformity, 12 morphological traits were measured in NP and the three selected populations. Genetic uniformity of these four populations was also evaluated using inter-simple sequence repeat (ISSR) markers. Results: Phenotypic uniformity was only exhibited with respect to target characters, including stem color in PSP and GSP, and petiole-peduncle angle in ETP. Average coefficients of variation detected for most non-target characters in PSP, GSP, and ETP were similar or higher to those of NP. When these four populations were analyzed using 129 ISSR markers, the percentage of polymorphic bands detected was 72.27% in PSP, 76.40% in GSP, 58.34% in ETP, and 76.23% in NP. Genetic identities (I) in PSP, GSP, and ETP were 0.9058, 0.8663, and 0.8703, respectively, with a value greater than 0.7814 in NP. Conclusion: Mass selection is an efficient way to improve target characters and genetic uniformity in P. notoginseng. Nevertheless, selection of specific individuals exhibiting comprehensive phenotypic traits may be necessary to accelerate the breeding process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".