Adventitious shoot regeneration from leaves of apple rootstock ‘Pingyitiancha’ (<i>Malus hupehensis</i>var.<i>pinyiensis</i>) and genetic fidelity of regenerated plantlets using SSR markers
Bibliographic record
Abstract
Jin, W., Wang, Y. and Wang, H. 2014. Adventitious shoot regeneration from leaves of apple rootstock ‘Pingyitiancha’ (Malus hupehensis var. pinyiensis) and genetic fidelity of regenerated plantlets using SSR markers. Can. J. Plant Sci. 94: 1345–1354. Apple is one of the major fruit tree species in China, its cultivation area and total output rank first in the world. ‘Pingyitiancha’ (Malus hupehensis var. pinyiensis) is a widely used rootstock for apple cultivation in China. Several factors affecting leaf regeneration were investigated. In this study, a successful adventitious shoot regeneration protocol for this cultivar was established. ‘Pingyitiancha’ leaves were a suitable source of explants for regeneration of adventitious shoots. The optimal adventitious shoot regeneration protocol involved subculturing seedling leaves for 30–35 d. The optimum medium was Murashige and Skoog (MS) medium containing 2.0 mg L−1thidiazuron and 0.2 mg L−1indole-3-butyric acid. Explants with the abaxial surface in contact with the medium kept for 14 d in the dark showed the highest regeneration percentage of adventitious shoots of explants (100%), and produced an average of 3.6 shoots per regenerating explant. Shoots regenerated from leaves were rooted on half-strength MS medium containing 0.4 mg L−11-naphthalene acetic acid. The rooting percentage was 94.4%. Using SSR markers, all banding profiles from regenerated plantlets were monomorphic and same to those of the mother plant. It showed that the uniformity of the in vitro regenerated plantlets was maintained.
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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 | 0.001 |
| 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".