Comparison of Safflower Cultivation in Two Seasons in the South of Brazil
Bibliographic record
Abstract
The objective of this work was to evaluate the production components, grain yield and safflower character correlations as a function of two sowing seasons (autumn and winter). Safflower culture was implanted in 2014 in a randomized complete block design with three replicates. The evaluation of safflower crop occurred in two seasons of the year, with the first growing season being characterized by sowing on April 30, 2014 (autumn), and the second growing season on July 30, 2014 (winter). The evaluations occur when the plants showed a 50% flowering and the following determination: plant height, number of branches per plant and chapters, stem diameter, dry weight of the stalk, dry mass of the branches dry mass of chapters and mass dry roots, grain yield, oil content and oil productivity. Growth stations were compared at 5% probability of error by the Student t test. For the study of correlations, Pearson’s linear correlation coefficient matrix (r) was estimated between the characters by means of the Student’s t-test, with a 5% probability of error. Except for the oil content and number of branches, safflower characters were benefited by early sowing in autumn. Safflower cultivated in autumn produced 3,820 kg ha-1 and in winter yielded 2,068 kg ha-1. For the early cultivation of autumn, the characters have greater correlation, favored by the climatic conditions. Grain and oil productivity obtained correlation higher than 97% in both seasons.
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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 | 0.001 |
| 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) | 0.000 | 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 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".