Stability yield indices on different sweet corn hybrids based on AMMI analysis
Bibliographic record
Abstract
Currently, sweet corn is considered an important crop due to its high sugar content and low starch content. Important sugars in sweet corn include sucrose, fructose, glucose, and maltose. The purpose of the present study was to use the yield indices of the eight examined sweet corn hybrids and the correlation of the yield indices together. Concentration is important for consumers in terms of yield indices. The research site was located at the Látókép Experimental Station of the University of Debrecen. The small plot experiment had a strip plot design with four replications. The previous crop was sweet corn; the plant density was 64 thousand/ha. The obtained result indicates that Biplot AMMI based on IPCA1 showed that the DB, NO, GS, and GB hybrids had stability and high performance in terms of yield indices. At the same time, fructose and glucose had stable parameters for the hybrids involved in the study. IPCA1 AMMI biplot showed that the ME hybrid had stability and high performance in terms of iron and zinc as well. IPCA2 AMMI biplot showed that DE, GB, and GS hybrids had stability and the highest performance on yield parameters in the scope of the research. Fructose, glucose, and sucrose had stable parameters on hybrids based on IPCA2. The DB and SE hybrids had desirable performance in Lutein and Zeaxanthin based on the biplot. The DE hybrid had a maximum performance on iron and zinc parameters.
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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 | 0.000 |
| 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 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".