Bibliographic record
Abstract
Publicly supported soybean [Glycine max (L.) Merr.] breeders evaluate their elite breeding lines in cooperative tests across the northern soybean production area of the USA and Canada. These cooperative tests have been conducted for 60 yr, and virtually all publicly released cultivars have been evaluated in these tests. However, the progress made has not been reported. The objective of this research was to determine progress in elite line improvement in soybean adapted to northern soybean production areas across this 60‐yr period. Two‐year performance data for the three highest‐yielding entries in these tests were regressed on years that entries appeared in maturity group (MG) tests across a 60‐yr period. Rates of yield improvement in kg ha−1 yr−1 were 21.6 (MG 00), 25.8 (MG 0), 30.4 (MG I), 29.3 (MG II), 30.6 MG (III), and 29.5 (MG IV). In general, check cultivars were consistent in performance across years in which the checks were included in the tests. Plant height increased slightly for elite lines in MG I, but decreased significantly for elite lines in MGs II to IV. Plant lodging decreased significantly for elite lines in every MG test except MG I. Seed protein concentration decreased significantly for elite lines only in MG I (−0.29 g kg−1 yr−1) and II (−0.27 g kg−1 yr−1). Seed oil increased significantly in MG 00 (0.19 g kg−1 yr−1) and decreased significantly in MG III (−0.11 g kg−1 yr−1). The data demonstrate that soybean breeders have increased seed yield of cultivars by ≈1.0% yr−1, while significantly increasing resistance to plant lodging. Rates of yield improvement during the past 20 yr have been equal to or greater than rates of improvement in earlier years.
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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 | 0.001 | 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".