Methods of Soybean Genotypes Selection in Paraná State, Brazil
Bibliographic record
Abstract
The soybean crop presents several cultivars available. The performance of each cultivar in the field is associated with its genetic characteristics and the interaction of these with environment. Specific recommendations according to environment are made soybean cultivars release based on adaptability and stability analyzes. This research evaluated twelve soybean cultivars in the northern region of Paraná State Brazil, in order to recommend the most suitable and stable cultivar. The experiment was designed in complete randomized blocks four sites: Maringá, Floresta, Cambé and Apucarana, with four replications, in 2017/2018 growing season. Totaling 48 experimental units per site, that is, a total of 192 in the experiment. The variables evaluated were: one thousand grain mass, productivity, hectoliter weight, number of pods per plant and number of grains per plant. The cultivars were evaluated for adaptability and stability by the methodologies proposed by Lin and Binns (1988) and a bi-segmented regression method according to Cruz et al. (1989). The results indicated that the selection was more reliable when the two methodologies were used, due to their correlation coefficients. Soybean cultivar 3 presented promising behavior in regions studied.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".