Pedigree analysis of pre-breeding efforts in Trifolium spp. germplasm in New Zealand
Bibliographic record
Abstract
BACKGROUND: Prebreeding in plants is the activity designed to identify useful characteristics from wild germplasm and its integration in breeding programs. Prebreeding aims to introduce new variation into the populations of a species of interest. Pedigree analysis is a valuable tool for evaluation of variation in genebanks where pedigree maps are used to visualize and describe population structure and variation within these populations. Margot Forde Germplasm Centre (MFGC) is New Zealand's national forage genebank and holds a collection of ~ 75 species of the genus Trifolium, of which only a dozen have been taken through prebreeding programs. The main objective of this study was to construct pedigree maps and analyse patterns of relatedness for seven minor Trifolium species accessions contained at the MFGC. These species are Trifolium ambiguum, Trifolium arvense, Trifolium dubium, Trifolium hybridum, Trifolium medium, Trifolium subterraneum and the Trifolium repens x Trifolium occidentale interspecific hybrids. We present a history of Trifolium spp. prebreeding in New Zealand and inform breeders of possible alternative forage species to use. RESULTS: Pedigree data from accessions introduced between 1950 and 2016 were used and filtered based on breeding activity. Kinship levels among Trifolium spp. remained below 8% and no inbreeding was found. Influential ancestors that contributed largely to populations structure were identified. The Australian cultivar 'Monaro' had a strong influence over the whole population of accessions in T. ambiguum. T. subterraneum and T. repens x T. occidentale had the largest number of generations (3). T. ambiguum and T. medium had the highest cumulative kinship across the decades. CONCLUSIONS: We conclude that there are high levels of diversity in the seven Trifolium spp. studied. However, collection and prebreeding efforts must be strengthened to maximize utilization and bring useful genetic variation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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".