Genotype × Environment Interaction and Stability Analysis for Watermelon Fruit Yield in the United States
Why this work is in the frame
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Bibliographic record
Abstract
One of the major breeding objectives for watermelon ( Citrullus lanatus [Thumb.] Matsum & Nakai) is improved fruit yield. High yielding genotypes have been identified, so we measured their stability for fruit yield and yield components over diverse environments. The objectives of this study were to (i) evaluate the yield of watermelon genotypes over years and locations, (ii) identify genotypes with high stability for yield, and (iii) measure the correlations among univariate and multivariate stability statistics. A diverse set of 40 genotypes was evaluated over 3 yr (2009, 2010, and 2011) and eight locations across the southern United States in replicated trials. Yield traits were evaluated over multiple harvests, and measured as marketable yield, fruit count, percentage cull fruit, percentage early fruit, and fruit size. There were strong effects of environment as well as genotype × environment interaction (G×E) on watermelon yield traits. Based on multiple stability measures, genotypes were classified as stable or unstable for yield. There was an advantage of hybrids over inbreds for yield components in both performance and responsiveness to favorable environments. Cultivars Big Crimson and Legacy are inbred lines with high yield and stability. A significant ( P < 0.001) and positive correlation was measured for Shukla's stability variance (σ i 2 ), Shukla's squared hat ( ŝ i 2 ), Wricke's ecovalence ( W i ), and deviation from regression ( S 2 d ) for all the traits evaluated in this study.
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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 | 0.000 |
| 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 it