Grain Yield in Indirect Selection for Multiple Characters in Upland Rice
Bibliographic record
Abstract
The aim of the present study was to compare the selection performed by the sum of standardized variables index (Z Index) with the selection based only on the grain yield character, to verify if the grain yield alone is a good alternative for the selection involving multiple characters. The experiments were conducted in Lavras-MG and in Lambari-MG, during the 2015/2016 agricultural year. The used design was the randomized complete block design with 3 replications. Thirty-six genotypes of the preliminary trial of upland rice breeding program of the Federal University of Lavras were evaluated. In order to compose the Z index, the following characteristics were evaluated: grain yield, height, number of days for flowering, 1000-grain weight, income, yield, leaf blast incidence, and grain length/width ratio. Z index was efficient in the selection for multiple characters whereas not all lines with the highest grain yield obtained good results in the other desirable characteristics, indicating that the selection based only on grain yield is not efficient when working with several characters of interest in upland rice cultivation.
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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) | 0.000 | 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) | 0.001 | 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".