Wide range QTL analysis for complex architectural traits in a 1-year-old apple progeny
Bibliographic record
Abstract
The present study aimed at investigating the genetic determinisms of architectural traits in a 1-year-old apple (Malus x domestica Borkh.). F1 progeny. A precise phenotyping including both tree topology and geometry was performed on 123 offspring. For a wide range of developmental traits, broad-sense heritability was estimated and quantitative trait loci (QTLs) were investigated. Several loci controlling geometry were identified (i) for integrated traits, such as tree surface and volume; (ii) for traits related to the form of long sylleptic axillary shoots (LSAS), such as bending and basis angle; and (iii) for traits of finer components, such as internode length of the trunk and LSAS. Considering topology, 4 QTLs were mapped for the total number of sylleptic branching in the tree, suggesting a strong and complex genetic control that was analysed through colocalisations between QTLs mapped for the different shoot types (long, medium, short). Two QTLs were also mapped for a phenological trait (date of bud break). When several QTLs were detected for a trait, a linear model was built to test epistatic effects and estimate the whole percentage of variability explained. The discussion focuses on particular colocalisations and on the relevance of traits to further tree development.
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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 | 0.000 | 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 | 0.000 |
| 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".