Characterization and pre‐breeding of diverse alfalfa wild relatives originating from drought‐stressed environments
Bibliographic record
Abstract
Abstract Changing climates and associated increased variability pose risks to alfalfa ( Medicago sativa L.) cultivation, with the requirement to establish, survive, and maintain production under water stress. Crop wild relatives (CWR) of alfalfa include populations that have evolved to survive in a number of different, extreme environments, but until recently have had limited use in breeding programs. Here we report on the phenotypic diversity of alfalfa crop wild relatives that were selected to represent extremes in drought tolerance (by sourcing germplasm from environments with extremes in low rainfall, high temperature, shallow soils, and winter freezing) with the aim of providing germplasm with drought tolerance and improved forage yield traits for breeding programs in both warm and cool dry temperate environments. Newly formed hybrids created between M. sativa , M. arborea L. (a woody shrub), and M. truncatula Gaertn. (an annual species from the Mediterranean region) were developed or acquired to introduce new genetic diversity from the tertiary genepool. Preliminary characterization and evaluation was used for taxonomic classification, and to identify wild accessions and pre‐bred (hybrid) lines that offer new diversity for growth habit, seed size, fall dormancy, and forage yield. The accessions and pre‐breeding lines described have been donated to the Australian Pastures Genebank for conservation and distribution.
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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) | 0.001 | 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".