Comparison of Models in Assessing Relationship of Corn Yield with Plant Height Measured during Early- to Mid-Season
Bibliographic record
Abstract
Relationship of corn (Zea mays L.) yield with plant height measured during early- to mid-season may possess the potential to be used to develop algorithms for guiding variable-rate N applications within a field. This study evaluated the performance of the linear, quadratic, square root, logarithmic, and exponential models in assessing the relationship of corn yield with plant height measured at three growth stages and four cropping systems for three years. The determination coefficient (R2) values of these five models were generally similar at each growth stage within each cropping system and year or within each cropping system on the three-year combined data. Our results suggest that all these models could be used to describe the relationship of corn yield with plant height during early- to mid-season under different cropping systems and weather conditions, but the linear model may be the preferred model because of its simplicity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".