The influence of seed size on soybean yield in short-season regions
Bibliographic record
Abstract
Two field experiments were done to determine if simple mechanical selection for seed size in soybean (Glycine max L. Merr.) could improve emergence and yield in short-season regions. Stacked screens with round holes 7.54, 7.14, 6.35 and 5.95 mm in diameter classified seeds into large, medium, small, and unscreened sizes. In exp. 1, small seeds resulted in lower yield than medium, large and unscreened seeds in two out of three cultivars, while in the third cultivar small seeds yielded less than the medium and large seed sizes, but were not significantly different from the unscreened size. In exp. 2, soil texture and seed size had a variable influence on seed emergence, which did not translate into consistent significant yield differences. Future research into mechanically removing small seeds from commercial seed lots is warranted in the short-season region. Key words: Seed size, soil texture, soybean, Glycine max L. Merr.
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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 | 0.000 |
| 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) | 0.001 | 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".