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 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 | 0.001 | 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".