Partitioning of General and Specific Combining Ability Effects for Estimating Maternal and Reciprocal Effects
Bibliographic record
Abstract
General and specific combining ability effects were partitioned according to a proposed model to estimate general and specific combining ability effects for each parent when it is used as a female or a male in its hybrid combinations. A working example includes a full-diallel among eight parents was used so that all possible hybrid combinations were included. The objectives of this study were: (1) to compare the GCA and SCA effects before and after partitioning, (2) to evaluate the relative contribution of each parent to its cross combination when it is used as a male or female parent, (3) to estimate maternal effects in the form of GCA and SCA effects, and (4) to estimate the relationship between maternal and reciprocal effects. Results revealed that estimated GCA effects according to Griffing’s method is equal to the average of GCA effects of each parent, after partitioning, when it is used as a male and a female in its hybrid combinations. In addition, the average of the difference between female and male GCA effects would provide precise estimation of the maternal effect. This would prove that maternal effect provides precise estimation to the favorable alleles, which is mainly additive. The SCA effects calculated according to Griffing’s method is the average of SCA effects of each cross and its reciprocal. The average of the difference between SCA effects of each cross and its reciprocal, according to the proposed model, is equal to the reciprocal effect. This would prove that reciprocal effect provides precise estimation to the interaction effect between nuclear and cytoplasmic genes given that the interaction between male and female alleles inside the nucleus of the cross is similar to its reciprocal hybrid.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".