Combining the optimal grafting methods, seasons, and scion donors enhances the conifer grafting success and early supply of the next-generation seedlings
Bibliographic record
Abstract
Improving the efficiency of grafting enables next-generation seed orchards to be established within a shorter period, resulting in improvement of productivity of high-quality seedlings for afforestation. This study investigates the effects of grafting methods (top-cleft and side-veneer), seasonal variation of collecting scions, and genetic variation of graft scions on the grafting survival rates of Chamaecyparis obtusa (Sieb. et Zucc.) Endl. When comparing the grafting methods, the scion shoot elongation of top-cleft method (13.7 cm) was greater than the side-veneer method (8.9 cm), while the survival rate did not differ between the top-cleft (87.5%) and side-veneer methods (83.3%). When comparing the grafting seasons on the survival rate of top-cleft grafts, the scions collected during the autumn and early spring showed significantly higher survival rates than those collected during the summer. In addition, there was a variation in graft survival rates among the genotypes, and a significant positive correlation ( r = 0.56) was found between the estimated clonal value of graft survival and of the estimated breeding value of trunk volume in the original test sites, indicating that the genotype that grew well had a higher rate of grafting success. These results can serve as practical guidance for conifer grafting.
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 | 0.001 | 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 | 0.001 | 0.001 |
| 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".