Advances in the Collection and Utilization of Fresh-Eating Maize Germplasm Resources
Bibliographic record
Abstract
As the core foundation of fresh corn breeding, germplasm resources play a key role in the selection and industrial application of new varieties. This study summarizes the progress of the collection and utilization of fresh corn germplasm resources, focusing on the development of high-quality traits and the screening methods of stress-resistant resources. The study found that the global fresh corn germplasm resources show rich diversity in quality (such as sweetness and stickiness), resistance (such as drought resistance and salt tolerance) and nutritional traits (such as high zinc and high vitamin A). Modern technologies, including molecular marker-assisted selection, genomic selection and CRISPR/Cas9 gene editing technology, have significantly improved the screening efficiency and breeding accuracy of germplasm resources. In addition, the commercial development of local germplasm resources and the promotion of regionally adaptable new varieties have provided successful cases for market demand-oriented breeding. Future research needs to be further deepened in terms of germplasm resource protection, evaluation standardization, technological innovation and international cooperation to achieve sustainable utilization and industrial application of resources. This study provides a comprehensive reference for the development and utilization of fresh corn germplasm resources and points out the direction for breeding work and agricultural development.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".