Effects of serial grafting, ontogeny, and genotype on rooting of<i>Quercus rubra</i>cuttings
Bibliographic record
Abstract
Bud grafts, up to three series over 3 years, were made on seedling and tree rootstocks using scions from juvenile and mature northern red oak (Quercus rubra L.). Serial grafts on juvenile rootstock used buds collected from shoots developed from grafted scions of prior years. Rooting trials were performed in years 2 and 3 with shoot cuttings developed in situ on seedlings and trees and developed from successful grafts. Without grafting, cuttings from seedlings rooted more frequently and had more roots than cuttings from trees. Significant variation within maturation groups due to genotype and ontogeny obscured absolute between-group differences. Grafting scions of juvenile origins onto seedling rootstock had little effect on percent rooting and the number of roots for cuttings. Grafting onto seedling rootstock tended to increase rooting and the number of roots for cuttings from mature origins, but the effect was not progressive with increasing grafting series. Grafting onto mature rootstock did not affect rooting of cuttings from juvenile or mature origins collected in the first growing season after grafting, but cuttings from juvenile scions collected in the second growing season exhibited reduced percent rooting compared with cuttings from seedling controls. Results suggest that northern red oak buds are predetermined in their developmental fate relative to rooting parameters and are only minimally influenced by grafting. The true effect of grafting on the subsequent rooting of cuttings may be mediated through processes other than rejuvenation.
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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 | 0.000 |
| 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".