A genetic analysis of seed and berry weight in grapevine [Erratum: 2009 Feb., v. 52, no. 2, p. 215.]
Bibliographic record
Abstract
Fruit size and seedlessness are highly relevant traits in many fruit crop species, and both are primary targets of breeding programs for table grapes. In this work we performed a quantitative genetic analysis of size and seedlessness in an F1 segregating population derived from the cross between a classical seeded (Vitis vinifera L. 'Dominga') and a newly bred seedless ('Autumn Seedless') cultivar. Fruit size was scored as berry weight (BW), and for seedlessness we considered both seed fresh weight (SFW) and the number of seeds and seed traces (SN) per berry. Quantitative trait loci (QTL) analysis of BW detected 3 QTLs affecting this trait and accounting for up to 67% of the total phenotypic variance. QTL analysis for seedlessness detected 3 QTLs affecting SN (explaining up to 35% of total variance) and 6 affecting SFW (explaining up to 90% of total variance). Among them, a major effect QTL explained almost half of the phenotypic variation for SFW. Comparative analysis of QTLs for these traits reduced the number of grapevine genomic regions involved, one of them being a major effect QTL for seedlessness. Association analyses showed that microsatellite locus VMC7F2, closely linked to this QTL, is a useful marker for selection of seedlessnes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".