Yield and antiyield genes in common bean (<scp><i>Phaseolus vulgaris</i></scp>L.)
Bibliographic record
Abstract
Abstract High yield is the primary criterion for the development of new cultivars at the University of Guelph common bean breeding program. As a complex trait, yield is encoded by a number of genes contributing minor effects while also being significantly affected by environmental factors. Genes that increase yield with fixed resources have their effects by increasing input use efficiency. When suppressed, the BnMicEmUP gene has a positive effect on seed production in Arabidopsis . Preliminary work has shown that ortholog of this gene ( Phvul.009G190100 ) exists in common bean, and its expression was negatively correlated with yield in a field test of 10 navy bean cultivars with different yield potentials. The aim of this research was to characterize the Phvul.009G190100 gene and to develop gene‐based marker(s) to test for alleles associated with high yield in common bean. A database search identified a second yield‐related gene ( Phvul.009G202100 ) on the same chromosome (Pv09), which is a homolog to Phvul.009G190100 . Both genes contain a DUF1118 protein domain, which has the molecular characteristics of a basic leucine zipper (bZIP) transcription factor, based on in silico analysis. Temperature switch polymerase chain reaction (PCR) markers, which were developed for both genes, were significantly associated with yield and maturity in 42 bean genotypes belonging to different market classes. The work will benefit bean breeding programs by making them more efficient in selecting high yielding cultivars, and it will directly benefit bean producers through accelerated access to new, high yielding cultivars.
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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".