An empirical model for changes in the leaf area of maize
Bibliographic record
Abstract
Chen, G., Zhang, J., Liu, P. and Dong, S. 2014. An empirical model for changes in the leaf area of maize. Can. J. Plant Sci. 94: 749–757. Accurate predictions of the leaf area index (LAI) are critical for many crop growth simulation models and essential for simulating crop growth and yield. In this paper, we present a new empirical leaf area model that simulates LAI for different maize (Zea mays L.) varieties under different cultivation conditions. Based on leaf morphological development, the model describes the two processes of leaf growth: expansion and senescence. The effect of planting density and nitrogen on LAI was also simulated in the model. A nitrogen sensitivity parameter was used to distinguish the different varieties. The model predictions were compared with field measurements of LAI for four varieties under different conditions. The results showed that the new model can correctly simulate LAI for maize under different cultivation conditions. The sensitivity analyses revealed that the new LAI model was very sensitive to lle (the length of the ear leaf) and VN (fertilizer sensitivity parameters of cultivars). The new model facilitates the simulation of maize leaf growth and senescence at the population level.
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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 | 0.002 | 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 | 0.001 | 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 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".