Physiological Parameters Associated with Differences in Kernel Set Among Maize Hybrids
Bibliographic record
Abstract
Simulation models of development, dry matter accumulation, and grain yield in maize (Zea mays L.) require an accurate estimate of kernel set when grain yield is estimated as the product of kernel number and mean kernel weight. Number of ovules initiated per plant is commonly an order of magnitude greater than kernel number per plant at physiological maturity, but kernel number may limit grain yield when abiotic or biotic stresses occur during a period around silking, that is, the critical phase for kernel set. Kernel number at maturity is related to plant photosynthesis during the critical phase of kernel set and the impact of stress on kernel number can be expressed through the effects of stress on plant photosynthesis. Determination of kernel number in a maize model requires (i) delineation of the critical phase of kernel set, (ii) quantification of the impact of stress on kernel set during different periods of the critical phase for kernel set, (iii) effect of relative maturity of the maize genotype, and (iv) genetic parameters that determine differences in kernel set among maize hybrids (i.e., genetic coefficients). Kernel set may only be indirectly related to plant photosynthesis during the critical phase, but separating the establishment of kernel number from plant photosynthesis during this phase may actually result in a reduction in grain yield, as the smaller source/sink ratio during the grain-filling period may result in accelerated leaf senescence and reduced dry matter accumulation. In conclusion, simulation of grain yield in maize requires quantitative information on relationships between rate of plant photosynthesis or dry matter accumulation and kernel number per plant during various subphases of the critical phase of kernel set and genetic coefficients for parameters determining kernel set that differ among hybrids and maturity groups.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".