Maize Stalk Lodging: Flexural Stiffness Predicts Strength
Bibliographic record
Abstract
Late‐season stalk lodging in maize (Zea mays L.) is a major agronomic problem that has far‐reaching economic ramifications. More rapid advances in lodging resistance could be achieved through development of selective breeding tools that are not confounded by environmental factors. It was hypothesized that measurements of stalk flexural stiffness (a mechanical measurement inspired by engineering beam theory) would be a stronger predictor of stalk strength than current technologies. Stalk flexural stiffness, rind penetration resistance and stalk bending strength measurements were acquired for five commercial varieties of dent corn grown at five planting densities and two locations. Correlation analyses revealed that stalk flexural stiffness predicted 81% of the variation in stalk strength, whereas rind penetration resistance only accounted for 18% of the variation in stalk strength. Strength predictions based on measurements of stalk flexural stiffness were not confounded by hybrid type, planting density, or planting location. Strength predictions based on rind penetration resistance were moderately to severely confounded by such factors. Results indicate that stalk flexural stiffness is a good predictor of stalk strength and that it may outperform rind penetration resistance as a selective breeding tool to improve lodging resistance of future varieties of maize.
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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 | 0.001 |
| 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 | 0.000 | 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".