Identification of Ploidy Variation of Ponkan Embryo Rescue Plants
Bibliographic record
Abstract
China has abundant of local citrus resources, which had many seeds. The Seedless is the aim of the local citrus breeding. Triploid citurs resources usually obtained by crosssing breeding between the diploid and tetraploid plants. However, the resources of tetraploid citrus are limited. It has been reported that the hybrid between diploid and diploid plants can produce triploid citrus, which not only saves the breeding time, but also shortens the breeding cycles. In this project, flow cytometry detection assisted by embryo rescue system and SSR identification technology were used to improve the probability of obtaining polyploidy from the hybrid offspring, and provide technical support for obtaining polyploidy from large-scale hybridization between diploid and diploid. plants. Three diploid pollen cultivars were crossbred with Hunan local cultivar 8306, immature seeds were collected for embryo rescue to obtain regenerative plants, plant ploidy was detected by flow cytometry and stoma electron microscope, and offspring genotypes were identified by SSR. After 100 d hybrid between three diploid pollens and Ponkan 8 306, 405 plants were obtained by embryo rescue technique. And 70 plants survived in the greenhouse after transplantation. Through flow cytometry instrument and stomatal electron microscope inspection, the results showed that SSR analysis validated that there are 13 polyploid plants, including 1 tetraploid plant and 12 triploid plants. The ratio of polyploidy was 18.57%. This study is obtained triploid and tetraploid plants by Ponkan embryo rescue from diploid interspecific hybridization. In this study, the embryo rescue technology system and the early identification system for polyploid hybridization were established, and a lot of new polyploid germplasm were obtained.
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 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 | 0.000 |
| 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".