Growing Degree Days during the Late Reproductive Phase Determine Spike Density and Cognate Yield Traits
Bibliographic record
Abstract
Drought has become more frequent in central Asia causing large losses in cereal yield. To surmount the existing problem, it is imperative to emphasize early maturing varietal development. However, the impact of heat units on spike morphology and its relationship with yield potential is still unclear. Thus, the current investigation was carried out to test wheat lines and varieties for variation in total heat unit’s accretion for anthesis and maturity and to understand the manipulating impact of sunlight on spike morphology, grain yield and its cognate traits. Furthermore, the gene action controlling major traits inheritance, combining ability effects, heritability, and association studies were also estimated. Following the Half Sib/Full Sib approach 27 hybrids along with 12 parents were tested. Results depicted broad variation in genetic stock. Correlation study demonstrated that earliness negatively affects the yield, while positively influencing spike density. Genetic variances were greater than variances due to environment, pointing to higher heritability (>50%) for all the characters except for grain’s weight spike−1. The degree of dominance revealed that the partial and over-dominant type of gene action conditioned inheritance of investigated traits. Thus, earliness can be used as an indirect selection criterion for yield advance.
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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 | 0.000 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".