Analysis of Linkage on Interaction of Main Aspects (Genotype by Environment Interaction, Stability and Genetic Parameters) of 1000 Kernels in Maize (Zea mays L.)
Bibliographic record
Abstract
The assessment of 1000-kernel weight holds significant importance in determining maize grain yield, and elucidating its underlying genetic mechanisms is imperative for enhancing its overall performance. The material for the study consisted of 26 doubled-haploid (DH) maize lines obtained from crossing two cultivars with flint kernels. Lines were planted in the northern part of the Lower Silesia voivodship in Poland over ten years (2013–2022). The 1000-kernel weight was assessed. The purposes of the research were as follows: (1) to assess genotype by environment interaction (GEI by the additive main effects and multiplicative interaction (AMMI) model; (2) the selection of stable DH lines and environment-specific lines; and (3) the estimation of parameters related to additive and additive–additive gene interaction (epistasis). The results indicate the significant effects of genotype and environment, as well as the GEI, on the 1000-kernel weight. Estimates of additive gene action effects were statistically significant in every year of the study, except 2022. Estimates of epistasis (total additive-by-additive interaction) effects for 1000-kernel weight were statistically significant in 2013, 2015, and 2017 (positive effects), as well as in 2018 and 2020 (negative effects). The lines KN07 and KN10 are recommended for further inclusion in the breeding program due to their stability and highest average of 1000-kernel weight.
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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 | 0.000 |
| 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 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".