Genetic linkage mapping for molecular dissection of chilling requirement and budbreak in apricot (<i>Prunus armeniaca</i>L.)
Bibliographic record
Abstract
Commercial production of apricot is severely affected by sensitivity to climatic conditions, an adaptive feature essential for cycling between vegetative or floral growth and dormancy. Yield losses are due to late winter or early spring frosts and inhibited vegetative or floral growth caused by unfulfilled chilling requirement (CR). Two apricot cultivars, Perfection and A.1740, were selected for high and low CR, respectively, to develop a mapping population of F1 individuals using a two-way pseudo-testcross mapping strategy. High-density male and female maps were constructed using, respectively, 655 and 592 markers (SSR and AFLP) spanning 550.6 and 454.9 cM with average marker intervals of 0.84 and 0.77 cM. CR was evaluated in two seasons on potted trees forced to break buds after cold treatments ranging from 100 to 900 h. A total of 12 putative CR quantitative trait loci (QTLs) were detected on six linkage groups using composite interval mapping and a simultaneous multiple regression fit. QTL main effects of additive and additive x additive interactions accounted for 58.5% +/- 6.7% and 66.1% +/- 5.8% of the total phenotypic variance in the Perfection and A.1740 maps, respectively. We report two apricot high-density maps and QTLs corresponding to map positions of differentially expressed transcripts and suggested candidate genes controlling CR.
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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 | 0.001 |
| 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".