Defining a Phenotypic Variability and Productivity in Wild Type Red Clover Germplasm
Bibliographic record
Abstract
Abiotic and biotic factors can cause great damage to crops. So, a key approach is to investigate whether the crops’ wild relatives are more flexible to withstand abiotic and biotic stress. As well as to evaluate their phenotypic variability and productivity in response to changing climatic conditions. In this study, red clover germplasm was collected from natural red clover habitats and a field trial was arranged ex situ. Twelve phenotypic traits and their effects on final harvest were analysed in 2018–2019. Principal component analysis (PCA) demonstrated that the most important trait for biomass yield was the height of the plant during the first season of harvest (2018). Interestingly, that the most significant trait in the second year of harvest (2019) was growth habit. Meantime, two way-joining analysis was performed to extent of phenotypic variation within and among red clover accessions, based on the most important trait for biomass yield. We found three main groups based on variation in plant height: “cultivars”, “wilds” and “mediators”. This analysis leads to identify typical populations of wild type red clover, which has not been done yet. Finally, the feed value of each red clover accession was analysed. It was found that “cultivars” have a higher level of crude proteins, while “wilds” contains higher levels of crude fibre. This indicates that there is a relationship between plant structure elements and forage value which is particularly important to select a breeding material.
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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".