Technologies of Reproduction Terry Varieties of Clematis
Bibliographic record
Abstract
The paper presents issues of optimizing the technology of growing popular terry varieties of clematis (Clematis L.) in Russia. Trimming groups, bookmark features of flower buds are described. The issues of reproduction and agricultural technology when laying uterine plantations are considered. A comparative analysis of traditional breeding technology with innovative technology (in vitro). The optimal nutrient medium for the stages of micro propagation, cultivation and rooting, enriched with vitamins and other substances. In experiments on green cuttings, 8 terry varieties were used. Estimated rooting of green cuttings by standard propagation technology. Spring cuttings are recommended for the varieties Bellof Woking (83%), Empress (81%), Blue Light (79%) and give a high yield of planting material from one mother plant. Two varieties with low rooting were selected—Purpurea Plena Elegans, Multi Blue. In vitro breeding technology has been developed for them. Accounting and observation of the development of two terry clematis varieties in the tissue culture: Purpurea Plena Elegans, Multi Blue. The aim of our study is to develop a technology for clonally propagation for these varieties, as well as the adaptation of micro plants of these varieties to non-sterile conditions. Therefore, in vitro propagation is recommended for these two varieties. An optimal substrate composition has been developed for the adaptation of clematis plants propagated in vitro, as well as for cuttings. The article provides recommendations for planting, pruning and caring for uterine plantings of clematis with double flowers in sheltered ground.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".