Assessing the Representativeness and Repeatability of Test Locations for Genotype Evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT The success of a plant breeding program depends on many factors; one crucial factor is the selection of suitable breeding and testing locations. A test location must be discriminating so that genetic differences among genotypes can be easily observed, it must be representative of the target environments so that selected genotypes have the desired adaptation, and its representation of the target environment should also be repeatable so that genotypes selected in 1 yr will have superior performance in future years. Using the yield data of 2006 through 2010 Quebec Oat Registration and Recommendation Trials as an example, we presented a method to visualize the representativeness and repeatability of test locations based on a genotype main effect plus genotype × environment interaction (GGE) biplot. The repeatability of a test location could also be quantified by mean genetic correlations between years within the location. Based on representativeness and repeatability, four categories of test locations were classified and their usefulness in plant breeding discussed.
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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 | 0.001 |
| 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) | 0.000 | 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