Physiological mechanisms underlying genetic improvement in sink establishment and plant‐to‐plant variability in maize
Bibliographic record
Abstract
Abstract Genetic improvement in maize (Zea mays L.) grain yield is associated with improvements in dry matter accumulation during the grain‐filling period and the ability to maintain partitioning to the grain (i.e., harvest index) when grown at higher plant population densities. Although several attributes have been identified that lead to improved dry matter accumulation during the grain‐filling period, the attributes that have enabled the maintenance of harvest index at higher plant population densities remain elusive. Using the Ontario ERA hybrids that represent five eras of genetic improvement in Ontario, we examined genetic improvement in several attributes associated with sink establishment and partitioning to the grain. We show that the number of florets on an ear initial is not influenced by plant density, nor is there any evidence of a genetic improvement in floret number. There has been genetic improvement, however, in the ability to support kernel set at a lower threshold level of dry matter accumulation and to more efficiently set kernels at lower dry matter accumulation levels, such as those experienced at higher plant densities. Genetic improvement is evident for reduced plant‐to‐plant variability for dry matter accumulation, grain yield, kernel number, and plant growth rate around silking, but most notably for grain yield. Finally, we show that genetic improvement in reduced plant‐to‐plant variability for grain yield is the result of the lower threshold dry matter levels required for seed set and the improved resource utilization, which has led to greater stability of individual plant performance in a stand.
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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".