Heterosis and inbreeding depression studies in maize for grain yield and related characters
Bibliographic record
Abstract
Nearly 65% of the global maize acreage is planted with hybrids, yet the genetic basis of heterosis remains incompletely understood in many temperate germplasm pools. This research examined mid-parent heterosis (MPH) and inbreeding depression (ID) across 15 single-cross maize hybrids and their corresponding selfed generations (S₁, S₂) at the Ontario College of Agriculture, Guelph, Canada, during the 2022 and 2023 growing seasons. Grain yield, ear length, kernel rows per ear, 1000-grain weight, plant height, and days to silking were recorded. MPH for grain yield ranged from 18.3% to 47.6%, with the cross OCA-7 × OCA-12 showing the highest value. Inbreeding depression in S₂ reduced grain yield by 29.4–51.7% relative to the F₁. Grain weight per ear contributed most (28.4%) to overall heterosis. Results suggest that crosses between genetically distant flint and dent lines offer the strongest heterotic response, and that breeders should monitor ID beyond S₁ to gauge recombination potential.
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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 | 0.001 | 0.001 |
| 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 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".