[Pedigree and development of wheat varieties in Sichuan Province].
Bibliographic record
Abstract
Variety pedigree contains a lot of information, including parental origin, breeding methods, genetic relationship, and so on. Studying them could reveal the evolution characteristics and rules of breeding and ultimately guide practice. The pedigrees of 326 wheat varieties from 1936 to 2017 in the history of the Sichuan Province was collected and analyzed in terms of breeding methods, parental composition, changes of high frequency parents and backbone parents, genetic contribution, distribution of translocation lines and synthetic germplasms. Over the past 80 years since 1930s, breeders have selected 387 direct parents from a large number of materials, made 256 combinations by means of cross breeding, and have released 314 varieties from them, which contributed directly to wheat breeding and production in Sichuan. Wheat breeding experienced a process from utilizing landraces, introducing foreign germplasm to creating breeding materials independently; high-frequency parents and backbone parents used for breeding gradually changed in different stage of the breeding history. Synthetic germplasms contributed greatly to wheat breeding in recent years. The consistency of breeding objectives will inevitably lead to the loss of genetic diversity and the fragility of genetic basis. In the future, the protection and utilization of genetic resources should be strengthened. In this review, the development of wheat breeding in Sichuan was summarized through pedigree analysis, in order to provide a reference for future research.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".