Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Soybean ( Glycine max [L.] Merr.) holds an important position worldwide due to its high protein and oil content, and is a key source of human consumption and animal feed. However, soybean cultivation is confronted with the challenges of climate change and the need to increase yield and stress resistance. Genome-wide association studies (GWAS) are of great value in identifying key genetic loci associated with complex agronomic traits, including yield, stress resistance, nutritional quality and disease resistance. This review summarizes the progress made in soybean genomics through GWAS and elaborates on the loci and candidate genes that affect traits such as seed composition, plant height, and root development. Integrating the findings of GWAS into molecular breeding strategies such as marker-assisted selection (MAS) and genomic selection (GS) can promote the development of high-yield and climate-adapted soybean varieties. Furthermore, the combination of GWAS with advanced genomic tools and computational methods provides insights for future research. These research findings contribute to the sustainable improvement of soybean productivity to address the urgent need for global food sec。urity under environmental challenges
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it