Correlations and Path Analysis in Sunflower Grown at Lower Elevations
Bibliographic record
Abstract
Sunflower cultivation has great importance in Brazil, mainly for production of oil and animal feed. Studies on sunflower cultivar selection are important for crop expansion, contributing to better cultivar adaptation to different environments. Thus, the objective of this study was to evaluate the linear relations among sunflower (Helianthus annuus L.) morphological traits in a subtropical region with lower elevations and to identify traits that may assist in cultivar selection based on agronomic performance and path analysis. The experiment was performed during the 2017/2018 agricultural year in Santa Maria (latitude 29º71′ S, longitude 53º70′ W and 90 m altitude), southern Brazil. The experimental design was a randomized block with four replicates and eight cultivars: Syn 045, BRS 323, BRS G58, BRS G59, BRS G60, BRS G61, Multissol 02 and Catissol 03. Assessed traits were plant height, stem diameter, head diameter, thousand achenes weight, yield of achenes per head and number of achenes per head. Hereafter, associations between morphological traits and achene yield were verified by means of linear relations and path analysis. Thousand achenes weight and number of achenes per head exhibited linear relations and direct effects on achene yield in subtropical region at lower elevations. Head diameter does not present direct effect on achenes yield but it has direct effect on the number of achenes per head, indicating cause-effect relation and becoming an important alternative for indirect selection of sunflower cultivars.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".