Bibliographic record
Abstract
Breeding line selection, either for potential varieties or for useful parents, must be based on multiple breeding objectives (or traits). Varieties cannot have any major defects, while parents must have outstanding levels in at least one trait. Due to undesirable associations among breeding objectives, it is difficult to accomplish both tasks (variety selection and parent selection) through a single selection strategy. Additional complication results when a program is breeding for different end‐uses such that both high and low levels of a trait are desirable. The first purpose of this paper was to propose a comprehensive multitrait selection procedure that coherently combines independent selection, independent culling, and index selection so that all the aspects in breeding line selection are taken into consideration. A dataset of 150 oat ( Avena sativa L.) breeding lines with values evaluated for four quality traits (groat, oil, protein, and beta‐glucan concentrations) was used for illustration. A genotype by trait biplot is a useful tool for exploring multiple trait data and can aid in multitrait selection because it graphically displays the trait associations across, and the trait profiles of, the genotypes. Procedures are outlined to avoid possible misinterpretation of such a biplot when the biplot does not fully display the patterns.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".