Heterosis for Leaf CO<sub>2</sub> Exchange Rate during the Grain‐Filling Period in Maize
Bibliographic record
Abstract
Heterosis for grain yield in maize (Zea mays L.) manifests itself through its effects on the components of grain yield, dry matter accumulation at maturity, harvest index, and its effects on physiological processes underlying these components, such as leaf CO2 exchange rate (CER). The objectives of this study were (i) to quantify the pattern of leaf CER throughout the grain‐filling period in maize hybrids and their parental inbred lines, and (ii) to determine the mode of inheritance of leaf CER during the grain‐filling period. Studies were performed with 12 F1 hybrids and their seven inbred parents grown hydroponically in the field at the Cambridge Research Station, ON, Canada, in 2002. Data were recorded on leaf CER from silking to maturity, and grain yield, aboveground dry matter, and root dry matter at maturity. Mean leaf CER of hybrids was not different from that of their parental inbred lines at silking. However, significant differences became apparent 2 wk after silking and became increasingly larger as plants advanced toward maturity. In general, leaf CER differed among inbred lines but did not differ among hybrids. Combining ability analysis showed that predominantly additive genetic effects influence the expression of leaf CER late in the season. Finally, the maintenance of leaf CER throughout a plant's life cycle, rather than potential leaf CER, is positively associated with dry matter accumulation during the grain‐filling period and grain yield.
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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 | 0.000 |
| 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".