Genotype by Yield*Trait (GYT) Biplot: a Novel Approach for Genotype Selection based on Multiple Traits
Why this work is in the frame
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Bibliographic record
Abstract
Genotype selection based on multiple traits is a key issue in plant breeding; it has been dependent on setting a subjective weight for each trait in index selection and a subjective truncation point for each trait in independent culling, and the weights and truncation points can be highly subjective. In this paper we proposed and demonstrated a novel approach for genotype selection based on multiple traits, the genotype by yield*trait (GYT) biplot, where "trait" can be any breeding objective other than yield; it may be an agronomic trait, a grain quality, processing quality, or nutritional quality trait, or a disease resistance. The GYT biplot ranks genotypes based on their levels in combining yield with other target traits and at the same time shows their trait profiles, i.e., their strengths and weaknesses. Compared to existing methods, this approach is graphical, objective, effective, and straightforward. Underlying the GYT biplot approach is the paradigm shift that genotypes should be evaluated by their levels in combining yield with other traits as opposed to by their levels in individual traits. An oat dataset from multi-year multi-locations trials was used to demonstrate the GYT biplot approach.
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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 | 0.001 | 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 it