Analysis of Character Association of Quantitative Traits in Lupinus Species
Bibliographic record
Abstract
An evaluation of the agronomic performance of two lupin species (Lupinus albus and Lupinus luteus) was conducted at the Institute of Forage Crops (Bulgaria) during 2012-2014. The hightest positive correlations among the agronomic traits in white lupin were between number of pods per plant and seed weight per plant (r = 0.956); plant height with pod stem length (r = 0.935) and pod length (r = 0.934); seed weight per plant and number of pods per plant (r = 0.956). In yellow lupin relatively high phenotypic correlations were detected between number of pods per plant and seed weight per plant (r = 0.956); seed weight per plant and number of pods (r = 0.875) and number of seeds per plant (r = 0.927). Collecting data on the mutual relationships among individual yield components and their effect on the yield remains crucial for their optimisation and development of improved lupin genotypes with high quality and stable yields. Based on the trait associations it can be concluded that lupin breeders should pay attention to the traits such as pod length, number of seeds per plant and 1000 seeds mass when selecting high-yielding genotypes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".