Inbreeding Depression of Progenies of Castor Bean, From of the Variety FCA-PB, Results of Three Types of Pollinations
Bibliographic record
Abstract
Oil of castor bean despite its wide range of use, still present a relevant deficit in the Brazilian market. This deficit could be softened or extinguished with increased productivity, in which the genetic aspect has a great contribution. The most productive genotypes are the hybrid varieties, however for obtaining the hybrids pure lineages are required which have appropriate agronomic characteristics and that express the minimum of inbreeding depression. Thus, the objective of this research was to evaluate the inbreeding depression of castor bean progenies, from the cultivar FCA-PB, resulting from three types of pollination. The experiments were implanted in design in randomized blocks, in the 30 × 3 factorial scheme, being 30 progenies and 3 types of pollination (free, cross and self-pollination), in 2 environments (São Manuel and Araçatuba) and in 2 crops (2004/2005 and 2005/2006), with three replications. Inbreeding depression was estimated under the grain productivity variable. It was observed that cross-pollination presented grain productivity close to 2500 kg ha-1, in which the lowest coefficient of inbreeding was determined, zero (0). Opposed It self-pollination, in which resulted in lower productivity, around 2000 kg ha-1, and the highest inbreeding coefficient being of 0.77. The reflection of the coefficient of inbreeding on productivity presents inversely proportional behavior, ie, the measure that increases the coefficient of inbreeding decreases the grain yield.
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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 | 0.000 | 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) | 0.000 | 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".