An Evaluation of<i>Arabidopsis thaliana</i>Hybrid Traits and Their Genetic Control
Bibliographic record
Abstract
Heterosis is an important phenomenon in agriculture. However, heterosis often greatly varies among hybrids and among traits. To investigate heterosis across a large number of traits and numerous genotypes, we evaluated 12 life history traits on parents and hybrids derived from five Arabidopsis thaliana ecotypes (Col, Ler-0, Cvi, Ws, and C24) by using a complete diallel analysis containing 20 hybrids. Parental contributions to heterosis were hybrid and trait specific with a few reciprocal differences. Most notably, C24 generated hybrids with flowering time, biomass, and reproductive traits that often exceeded high-parent values. However, reproductive traits of C24 and Col hybrids and flowering time traits of C24 and Ler hybrids had no heterosis. We investigated whether allelic variation at flowering time genes FRIGIDA (FRI) and FLOWERING LOCUS C (FLC) could explain the genotype- and trait-specific contribution of C24 to hybrid traits. We evaluated both Col and Ler lines introgressed with various FRI and FLC alleles and hybrids between these lines and C24. Hybrids with functional FLC differed from hybrids with nonfunctional FLC for 21 of the 24 hybrid-trait combinations. In most crosses, heterosis was fully or partially explained by FRI and FLC. Our results describe the genetic diversity for heterosis within a sample of A. thaliana ecotypes and show that FRI and FLC are major factors that contribute to heterosis in a genotype and trait specific fashion.
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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 | 0.001 |
| 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".