Effect of Genotype, Nitrogen, Plant Density, and Row Spacing on the Area‐per‐Leaf Profile in Maize
Why this work is in the frame
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Bibliographic record
Abstract
Accurate estimates of total leaf area and the vertical leaf area profile are important in process‐based crop growth models. The bell‐shaped function that quantifies the area‐per‐leaf profile of a maize ( Zea mays L.) plant can be used to estimate the area‐per‐leaf profile. The objectives of this study were to quantify the effects of maize hybrid, soil N, plant density, and row spacing on the coefficients of the bell‐shaped function. The coefficients of the bell‐shaped function that quantify (i) the breadth of the area‐per‐leaf profile, (ii) the skewness of the area‐per‐leaf profile, and (iii) the position of the largest leaf were estimated using nonlinear regression in four datasets. Datasets consisted of the fully expanded leaf areas of all leaves on maize plants grown in studies performed in Ontario, Canada, between 1997 and 2001 that included combinations of maize hybrids, plant densities, N levels, and row spacing. Observations fitted well to the bell‐shaped function ( r 2 > 0.95). The breadth of the area‐per‐leaf profile decreased under high soil N level and high plant density, and was lower for a newer than an older hybrid, whereas the opposite occurred with the position of the largest leaf. In contrast, the degree of skewness was not significantly altered by any of the factors examined in this study. Because of the relatively small impact of the examined agronomic factors on the coefficients of the bell‐shaped function, a general model using mean coefficient values was validated with independent datasets. Results showed that this general bell‐shaped function is a robust predictor of the area‐per‐leaf profile in maize.
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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 | 0.001 | 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) | 0.000 | 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 it