Maternal and paternal contributions to the fitness of hybrids between red and white mulberry (<i>Morus</i>, Moraceae)
Bibliographic record
Abstract
The fitness of hybrids depends on the genetic disparity between parental taxa and the magnitude of their nuclear and non-nuclear contributions. To estimate the role of non-nuclear effects, we crossed red (R), white (W) and hybrid (H) mulberry in all combinations and compared the magnitude of maternal and paternal effects on offspring fitness (seed set, germination, survival and aboveground biomass) in a greenhouse environment. Variation in offspring fitness was determined largely by the identity of the maternal parent; specifically, progeny with white mothers had the highest cumulative fitness. As fathers, red, white, and hybrid mulberry had no effect on fitness, and maternal × paternal interactions were significant only for survival. Individual cross-types differed significantly for all fitness components except seed set. Offspring from hybrid crosses (W × R, H × R, H × W) often differed from at least one of the within-parent crosses (W × W, R × R) as well as from other hybrid crosses, although their fitness values never exceeded the most fit parent. Reciprocal crosses differed in only two of 15 possible parental combinations: W × H (cumulative fitness) and W × R (aboveground biomass). Overall, the strong asymmetry in magnitude of maternal and paternal effects suggests that fitness of hybrid mulberry is governed largely by non-nuclear, parental effects.
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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 | 0.000 | 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) | 0.001 | 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".