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 ) is a vital global crop valued for its protein and oil content, making the optimization of agronomic practices such as sowing density essential for improving both yield and quality. This study synthesized the current research results on the physiological, environmental and genotypic effects of different sowing densities on soybean growth and development, and explored how plant competition for resources, canopy structure and root development interact with sowing density to affect yield and seed quality traits such as protein and oil content, uniformity and vitality; further explored how environmental factors such as soil fertility, climate change and management practices regulate density response, and the differences in genotype adaptability to high-density and low-density planting. The case study in China provides localized insights for the practical application of optimizing sowing density, while future development directions emphasize the application of precision agriculture and remote sensing technology integration in density management. This study underscores the need for site- and variety-specific sowing strategies to enhance sustainable soybean production and guide future agronomic innovations.
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