Soybean Lutein Concentration: Impact of Crop Management and Genotypes
Bibliographic record
Abstract
Lutein is a carotenoid with health‐beneficial properties found in soybean [Glycine max (L.) Merr.]. A study was conducted in multiple environments in Quebec, Canada, to determine the effects of crop management practices and genotypes on soybean lutein concentrations. Practices evaluated included seeding rate, row spacing, seeding date, and P and K fertilization; lutein variation and stability among 20 genotypes were also studied. Management practices affected soybean lutein concentration to different degrees. Seeding date had the greatest effect on lutein concentration of all factors evaluated, but response varied greatly between environments. Differences in lutein concentration between seeding date treatments averaged 41%. Seeding rate, row spacing, and P and K fertilization effects were minimal. Increasing the seeding rate from 40 to 60 seed m−2 resulted in a 6% increase in lutein concentration. Response to row spacing and P fertilization treatment was inconsistent and differences between treatments were never >8%. There was no response to K fertilization. Large differences were observed between the 20 genotypes evaluated, with lutein concentrations ranging between 4.1 and 10.9 μg g−1 Despite the presence of significant environmental effects, genotypes with consistently high and stable lutein concentrations were identified. Selection and development of high‐lutein cultivars should be possible; however, environmental factors and crop management practices should be considered in the use of soybean as a source of lutein.
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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 | 0.001 |
| 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 | 0.001 | 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".