Bibliographic record
Abstract
Despite its inherent symbiotic N2-fixing (SNF) ability, the common bean (Phaseolus vulgaris L.), compared to other legumes, is generally known as a weak N2-fixer. The objectives of this thesis were to examine the genetic variation, environmental and genotype by environmental effects, and response to selection for SNF and related traits, as well as to identify genomic regions and candidate genes underlying SNF in common bean. Significant variation was found among bean genotypes for percentage of nitrogen derived from atmosphere (%Ndfa), a measure of SNF, and related traits. In studies of a recombinant inbred line (RIL) population, derived from a cross of high- and low-SNF genotypes, while SNF-dependent and fertilizer-dependent N management strategies did not affect the overall yield, genotypes responded differentially to N management across environments. Among the RILs, 6% maintained a stable yield, independent of N management strategies. Estimates of heritability and genetic gain were higher for %Ndfa and related traits in optimum moisture environments. Genotypes with longer vegetative growth had higher SNF ability. Quantitative trait loci (QTL) analysis detected 42 QTL for %Ndfa and related traits. In spite of the significant Genotype by environment interactions, a QTL was detected on Pv08 that was significantly associated with %Ndfa across environments, accounting for up to 17% of variation in %Ndfa. Another QTL was identified on Pv07, associated with %Ndfa only in the dry environments, which accounted for 14% of the phenotypic variation. This QTL was in close linkage with a QTL for δ13C, a measure of water use efficiency. Close genetic association between %Ndfa and δ13C QTL, either in the dry or optimum rainfall conditions, conformed to the positive association between SNF and water use efficiency, which emphasizes the importance of nodulation early in the growing season for efficient SNF in common bean. Three potential candidate genes were detected in the %Ndfa QTL region on Pv08, with potential roles in SNF.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".