Sugarcane Family Selection and Genetic Parameter Prediction via the REML/BLUP Methodology
Bibliographic record
Abstract
Most sugarcane breeding programs tend to evaluate low heritability characteristics during the initial stages of genotype selection. Thus, family selection has been recently preferred. In this context, the aim of the present study was to select the best family among 78 sugarcane families, as well as estimate genetic values through the mixed models of restricted-maximum likelihood and best non-bias predictor (REML/BLUP) methodology, originating from the República Brasil 2005 (RB05) series. This strategy was deemed efficient, and 34 to 38 families were chosen from four evaluated characteristics underexplored by genetic researchers such as total plot mass (MTT), mean mass of one tiller in the plot (M1C), stature (EST), and mean number of canes per square meter (NCM). The family increments ranging from 6.02 to 82.11%, in the next genetic culture improvement program selection phases.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".