Asymmetrical introgression between two<i>Morus</i>species (<i>M. alba</i>,<i>M. rubra</i>) that differ in abundance
Bibliographic record
Abstract
Asymmetrical introgression is an expected genetic consequence of hybridization when parental taxa differ in abundance; however, evidence for such effects in small populations is scarce. To test this prediction, we estimated the magnitude and direction of hybridization between red mulberry (Morus rubra L.), an endangered species in Canada, and the introduced and more abundant white mulberry (Morus alba L.) using nuclear (randomly amplified polymorphic DNA) and cytoplasmic (chloroplast DNA sequence) markers. Parentage of 184 trees (n = 42 using cpDNA) from four sympatric populations was estimated using a hybrid index and related to six morphological characters and population frequencies of the parental classes. Overall, the frequency of nuclear hybrids was 53.7% (n = 99) and ranged from 43% to 67% among populations. The parental and hybrid taxa differed with respect to all of the morphological traits. Sixty-seven percent of all hybrids contained more nuclear markers from M. alba than M. rubra (hybrid index x = 0.46); among populations, the degree of M. alba bias was correlated with the frequency of M. alba. In addition, the majority of hybrids (68%) contained the chloroplast genome of white mulberry. These results suggest that introgression is bidirectional but asymmetrical and is related, in part, to the relative frequency of parental taxa.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".