Adventitious root formation in <i>Castanea</i> sp. semi-hard cuttings is under moderate genetic control caused mainly by non-additive genetic variance
Bibliographic record
Abstract
Breeding programmes of European chestnut (Castanea sativa Mill.) for disease resistance have focused on interspecific hybridization followed by clonal propagation. Although chestnut species are recalcitrant to rooting by cuttings, it is possible to mass propagate new softwood cuttings taken from shoot stumps. To determine the importance of genetic control in adventitious root formation in chestnut, two trials were conducted with 705 ortets from 25 young full-sib families generated by combining 11 different parents including seven C. sativa, one C. crenata, one C. mollissima, and two F 1 hybrid (C. crenata × C. sativa) individuals. Rooting variables were analysed using an incomplete diallel model, and statistical analyses were performed using the MIXED and GLIMMIX procedures for continuous and binomial variables, respectively. Very high rooting percentages were obtained (91% and 82%) and the cuttings showed adequate numbers of roots (17 and 10); both characteristics were correlated ([Formula: see text] = 0.93) and presented moderate genetic control ([Formula: see text] = 0.33 for the presence of root (Proot); 0.43 and 0.35 for number of roots (Nroots)) due mainly to dominance and epistasis ([Formula: see text] = 0.21 for Proot; 0.23 and 0.27 for Nroots). There were good rooting ortets within all families, and accordingly, it is possible to select for rooting capability within each family, at least at a young age.
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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 | 0.000 | 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) | 0.001 | 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".