Similarity of maize seed number responsesfor a diverse set of sites
Bibliographic record
Abstract
Accurate modeling of maize (Zea mays L.) yields in diverse environments requires realistic simulation of seed numbers. Response of maize seed number to growth or light interception soon after pollination has been described with different types of functions. The objective of this study was to compare maize seed number responses to intercepted solar radiation or growth with data from a diverse set of sites. Pioneer hybrid 3394 planted near Temple, TX in 1999 at 2.5 to 20 plants$\cdot$m$^{-2}$ showed a linear function for seed number responses to light intercepted per plant in the 11 d following silking and to ear growth rate in these 11 d. Similar linear seed number responses were found for three hybrids in Canada at 4 to 13 plants$\cdot$m$^{-2}$. Likewise, the function for Pioneer 3394 in Temple was found to be similar to a regression for the same hybrid grown in Pennsylvania, and was similar to a function developed in Kenya. Thus, under the diverse environmental conditions of these studies, linear seed number functions appeared reasonable at these sites. Such seed number functions are critical to the understanding of optimization of planting density to maximum seed production per unit ground area. In the absence of drought stress, the optimum density will be the minimum planting density which could attain near-complete light interception at silking. As the probability of drought stress increases due to decreased soil water holding capacity or decreased expected rainfall, the optimum density would decrease accordingly.
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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 | 0.000 | 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 | 0.001 |
| 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".