10.53762/grjnst.03.02.04
Bibliographic record
Abstract
The present study comprises of 16 indigenous hybrids with two replications and evaluated in RCBD during Kharif 2023 to assess the hybrids for kernel yield and its associated traits at Faisalabad, Punjab, Pakistan. Analysis of variance revealed that plant height (PHt), ear height (ErHt), ear length (ErL), ear girth (ErG), kernel/row (KeRo), kernel length (KeL), kernel width (KeW), kernel thickness (KeTh), 100 kernel weight (KW), shelling % (Shell) showed highly significant variation, kernel yield (Y) possessed significant variation while days to 50% silking (Silk) showed non-significant variation among 16 hybrids. Pearson correlation analysis revealed that highly significant association found between PHt and KeW (0.72**), KW and ErL (0.68**), KeRo and KeTh (-0.64**). Significant correlation found between KW and KeRo (0.53*), ErL and KeRo (0.52*), ErG and KeL (0.57*), KW and KeTh (-0.51*), KeL and KeTh (-0.53*). The first two components of PCA accounted for 53.2 % of the total variance. PCA biplots arrow for Y aligns in the direction of ErG and KeL that showed significant association for Y improvement. Arrow head of PHt and ErHt point in the same direction, suggesting both traits can be improved simultaneously but they are oriented opposite to Y. Similarly, arrows of ErG and KeL point in the same direction, connecting positive relation between them which can be improved together. Kernel yield (Y) can be enhanced by improving ErG and KeL. Finally, FH-1720 and YH-5427 exhibited superior performance in Y, KeL and ErG, making them promising candidates for achieving higher grain yield.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.780 | 0.866 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".