Genetic diversity and local adaption of alfalfa populations (Medicago sativa L.) under long-term grazing
Bibliographic record
Abstract
Genomic information on alfalfa adaptation to long-term grazing is useful for alfalfa genetic improvement. In this study, 14 alfalfa populations were collected from long-term grazing sites (> 25 years) across four soil zones in western Canada. Alfalfa cultivars released between 1926 and 1980 were used to compare degree of genetic variation of the 14 populations. Six agro-morphological and three nutritive value traits were evaluated from 2018 to 2020. The genotyping-by-sequencing (GBS) data of the alfalfa populations and environmental data were used for genotype-environment association (GEA). Both STRUCTURE and UPGMA based on 19,853 SNPs showed that the 14 alfalfa populations from long-term grazing sites had varying levels of parentages from alfalfa sub-species Medicago sativa and M. falcata. The linear regression of STRUCTURE membership probability on phenotypic data indicated genetic variations of forage dry matter yield, spring vigor and plant height were low, but genetic variations of regrowth, fall plant height, days to flower and crude protein were still high for the 14 alfalfa populations from long-term grazing sites. The GEA identified 31 SNPs associated with 13 candidate genes that were mainly associated with six environmental factors of. Candidate genes underlying environmental factors were associated with a variety of proteins, which were involved in plant responses to abiotic stresses, i.e., drought, cold and salinity-alkali stresses.
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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 | 0.001 |
| 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) | 0.000 | 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 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".