Selection Strategies for Grain Quality in Upland Rice Lines
Bibliographic record
Abstract
The grain quality in rice is extremely important for breeding programs, in order to select lines that meet the standards demanded by the market. The quality attribute is composed by several characteristics, which can make difficult the work for breeder. Thus, the objective in this study was to verify the efficiency of the selection indexes in order to select upland rice lines that meet the grain quality standards. Fourteen lines of the Cultivation and Use Value test (CUV) of the upland rice breeding program of the Federal University of Lavras were evaluated. The experiments were conducted in the municipalities of Lavras-MG and Lambari-MG in the seasons of 2015/2016 and 2016/2017. The experimental was a randomized block design with three replications. The following characteristics were evaluated to compose the indexes: grain yield, minimum cooking time, water absorption index, grain chalkiness, integer grain percentage, grain length and width, apparent amylose content and gelatinization temperature. The following selection indexes were compared: Base index of Willians, Sum of “Ranks” of Mulamb and Mock, and Index Sum of Standardized Variables (Z Index) and also was held direct selection through grain yield. It was observed that the Base index obtained good gains with the selection for grain yield, but it was inefficient for the quality characteristics, results when used direct selection. The Ranks and Z index obtained superior and balanced gains for the characteristics, showing up more efficient in the selection of upland rice lines aiming at the quality of grains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".