Interstock effects on topgraft vitality and strobili production after topgrafting in<i>Pinus sylvestris</i>
Bibliographic record
Abstract
Reducing generation turnover in breeding programs should increase the genetic gain per unit time. This could potentially be achieved by topgrafting, since it can be used to induce strobili production in young plant material. I therefore studied the effect of the interstock clone on the vitality and strobili production of topgrafts in Pinus sylvestris L. over 4 years. The experiment consisted of 20 interstock clones × 3 ramets per interstock × 10 topgrafts per ramet, giving a total of 600 topgrafts. There were large differences in topgraft survival rates among the different interstock clones, ranging from 60% to 93% in the spring of the experiment’s fourth year. Analysis of variance (ANOVA) revealed that both the interstock and the topgraft had significant effects on female and male topgraft strobili production. The effect of the ramet within the interstock was small on topgraft vitality but was large and significant on strobili production. The interaction between interstock and topgraft was not significant for topgraft vitality and male strobili production for any of the studied years, but it was significant two out of four years for female strobili production. No relationship was found between female strobili production on the interstocks and their capacity to induce female strobilis on topgrafts, but there was a weak relationship between strobili production on the interstocks and their capacity to induce male strobilis on topgrafts. Overall, the results indicate that topgrafting could, with appropriate interstock clones, effectively decrease the time to strobili production compared with conventional grafting using young rootstocks in P. sylvestris, but suitable interstock clones will have to be identified by screening tests.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".