Model Concepts to Express Genetic Differences in Maize Yield Components
Bibliographic record
Abstract
Maize ( Zea mays L.) grain yield is closely related to kernel number per unit area. The quantification of genetic differences among maize cultivars to kernel number plant −1 (KNP) is critical for accurate yield simulation but remains one of the less accurate components of yield modeling. Our objective was to document the recently published KNP data and revise CERES Maize model (V3.5). The duration of a critical window for KNP simulation was 327°C days (227°C days before and 100°C days after silking—base temperature 8°C) when ears actively grew. The KNP was curvilinearly related to cumulative intercepted photosynthetically active radiation plant −1 (CIPAR) during the critical window. Potential kernel ear −1 and kernel produced per unit CIPAR were the genetic coefficients needed to simulate KNP. Apical ears produced maximum KNP at a plateau CIPAR of 64 MJ, and prolific hybrids produced secondary ears when CIPAR exceeded 64 MJ. The genetic differences in prolificacy in low plant density were expressed by another coefficient. Below a threshold CIPAR of 11 MJ, all plants were barren, and a barrenness coefficient expressed genetic differences among old and modern hybrids to produce KNP in high plant density. Sensitivity analysis with limited testing indicated that the revised model simulated yield reasonably well [root mean square error (RMSE) = 0.63 Mg ha −1 ] compared with the original model (RMSE = 1.25 Mg ha −1 ) across a wide range of plant densities. However, rigorous testing of the model will be required to gain greater confidence in the proposed concepts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".