Sunflower Genotype Selection for Oil Production in the Pre-Amazon Region of Brazil
Bibliographic record
Abstract
Part of the energy consumed in the world comes from limited sources, which eventually are expected to be depleted. The search for alternative sources to meet energy needs is crucial. Biodiesel derived from vegetable oils and animal fats stands out as a biodegradable and renewable alternative source of energy. Sunflower is among the top four oil crops produced worldwide, and Brazil has a high production potential for this crop. This study aimed to identify the sunflower genotypes with the highest potential for biodiesel production in the Pre-amazon region of Brazil, where the advance of agricultural frontier represents an important role on biodiesel production. This study was conducted over 2 years of observations. The following genotypes were used: M734 (T), Helio358, EMBRAPA 122, and BRS G 35. The following parameters were assessed: initial flowering date (IFD), physiological maturity date (PMD), plant height (PH), grain yield (YIELD), thousand achene weight (TAW), and oil content (%OC). The climatic conditions of each experimental period were distinctive and directly affected the results obtained. The genotype Embrapa 122 showed the best performance regarding yield and was recommended for the pre-Amazon region of Brazil as the best adapted genotype to the local environmental conditions.
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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 | 0.001 |
| 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".