Size Variability in Seed Lot Impact Seed Nutritional Balance, Seedling Vigor, Microbial Composition and Plant Performance of Common Corn Hybrids
Bibliographic record
Abstract
Soils with highly uniform textural, physical, and chemical characteristics still give rise to crop stand variability. Seed quality is one of the factors adding to yield variability and has become a concern for corn growers. Hybrid seed producers claim that their seeds provide a uniformity in crop emergence and productivity, but they do not always provide detailed studies to support this claim. Based on growers’ concerns, we examined fields planted with three different hybrid varieties and found that 25% to 50% of the stand had relatively weak vigor, where seed variety A showed 15% of seedlings with lower vigor, and varieties B and C had 30% of seedlings with low vigor. These apparent differences in plant vigor prompted us to initiate a cursory investigation to identify how seed size influenced seedling vigor and if the seedling’s microbial profile played a role in the early growth stages of three commonly grown corn hybrids in Ontario. Seeds were separated based on size, prior to conducting a growth room study. Different sizes of seeds from the same seed lot showed significant differences in vigor capacity and related biometric components. Significant differences were also found in their nutritional composition and microbial profiles within the different seed sizes and the roots and shoots of seedlings derived from such seeds. The results clearly indicate that seed size greatly impacts the plant growth and its microbiome, resulting in seedlings with different plant vigor, microbiomes, and performance.
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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".