Bibliographic record
Abstract
ABSTRACT Despite widespread use of early generation testing and selection (EGT) in breeding for self‐pollinated crops, its effectiveness remains largely an unresolved issue. This issue is tackled here using elaborated genetic models that enable genetic and nongenetic effects to be assessed for the effectiveness of EGT in terms of (i) the selection response at one or more early generations relative to the response to direct selection at homozygosity and (ii) the probability of retaining superior lines selected during EGT. The selection response to EGT is analyzed for a model with quadratic genetic components due to additive, dominance, additive × additive, and linkage effects in selfed populations derived from a cross between two inbreds. The response to one cycle of EGT is less than the response to direct selection and decreases with nonadditive effects, repulsion linkage and reduced heritabilities. The cumulative response to two or more cycles of EGT is greater than the response to direct selection unless there are strong nonadditive effects, strong repulsion linkage, and low heritabilities. The probability of retaining a superior line at EGT decreases with increased nonadditive effects and low heritabilities. The proportion of lines needed to minimize the risk of erroneously culling superior lines increases with increased nonadditive effects and low heritabilities. Thus, EGT should be used for populations or traits with little nonadditive effect, coupling linkage and high heritability.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".