Grain Yield and Its Associations With Pre-harvest Sprouting in Wheat
Bibliographic record
Abstract
This study aimed to evaluate direct and indirect effects of agronomic traits importance on grain yield with focus in pre-harvest sprouting. Experiment was conducted in 2017 crop season, and conducted in a randomized block design, with three replications, with eight wheat cultivars (BRS Sábia, CD 105, CD 1104, CD 1440, Frontana, Jadeíte 11, Mirante and ORS Vintecinco). Grain yield and its components were evaluated, as well as other important traits such as pre-harvest sprouting. Data were submitted to variance analysis; and phenotypic, genotypic and environmental correlations were estimated to understand grain yield expression, direct and indirect effects of its components were evaluated through path analysis. Cultivar BRS Sabiá showed shorter cycle, cultivar CD 1104 was highlighted in number of spikes per area, hectoliter weight and grain yield. And cultivars Frontana, CD 1440 and ORS Vintecinco presented the best tolerances pre-harvest sprouting. Grain yield showed high and positive phenotypic and genotypic correlations with number of ears per square meter. Furthermore, high positive direct effect of pre-harvest sprouting on grain yield, revels lower tolerance for cultivars with high grain yield. Number of spikes per square meter showed intermediate and positive direct effect and pre-harvest sprouting had the greatest direct effect on 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 | 0.000 | 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".