Meta-Analysis of Genetic Diversity in Global Fresh-Eating Maize Germplasm Resources
Bibliographic record
Abstract
As an important food and cash crop in the world, the genetic diversity of fresh corn germplasm resources is of great significance for variety improvement and genetic improvement. This study systematically sorted out the research data of fresh corn germplasm resources around the world through meta-analysis methods, covering multi-level information such as morphological characteristics, molecular markers and whole genome data. The study comprehensively evaluated the genetic diversity characteristics of germplasm resources in different geographical regions, and explored the impact of cultivation history, ecological environment, artificial selection and gene flow on genetic diversity. The study compared the results of different genetic diversity analysis methods (such as phenotypic data analysis, molecular marker analysis and whole genome high-throughput analysis), and analyzed the applicability, advantages and limitations of each method. The results show that fresh corn germplasm resources have high genetic diversity worldwide, but the diversity is declining due to factors such as habitat loss, genetic drift and single breeding. In terms of protection and utilization, this study proposed methods for the discovery and evaluation of excellent germplasm resources, suggested strengthening the sharing and cooperation of germplasm resources worldwide, and explored new paths for interdisciplinary research and data integration. The research results not only provide theoretical support for the protection of fresh corn germplasm resources, but also provide an important basis for future breeding work and genetic improvement strategies.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".