Genetic analysis of photosynthesis‐related traits in faba bean (<i>Vicia faba</i>) for crop improvement
Bibliographic record
Abstract
Abstract Increasing productivity through improvement of photosynthesis in faba bean breeding programmes requires understanding of the genetic control of photosynthesis‐related traits. Hence, we investigated the gene action of leaf area, gas exchange traits, canopy temperature, chlorophyll content, chlorophyll fluorescence parameters and biomass. We chose inbred lines derived from cultivars 'Aurora' (Sweden) and 'Mélodie' (France) along with an Andean accession, ILB 938, crossed them (Aurora/2 × Mélodie/2, ILB 938/2 × Aurora/2 and Mélodie/2 × ILB 938/2), and prepared the six standard generations for quantitative analysis (P1, P2, F1, F2, B1, and B2). Gene action was complex for each trait, involving additive and dominance gene actions and interactions. Additive gene action was important for SPAD, photosynthetic rate, stomatal conductance and Fv/Fm. Dominance effect was important for biomass production. It is suggested that breeders selecting for productivity can maximize genetic gain by selecting early generations for canopy temperature, SPAD and Fv/Fm, then later generations for biomass. The information on genetics of various contributing traits of photosynthesis will assist plant breeders in choosing an appropriate breeding strategy for enhancing productivity in faba bean.
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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".