Simple inheritance of a complex trait: figured wood in curly birch is caused by one semi-dominant and lethal Mendelian factor?
Bibliographic record
Abstract
Even though individuals with a deviant morphology have been elemental in genetics of model species, they have thus far been largely ignored in the studies of forest trees. Here we studied the inheritance of curly-grained and brown-figured wood phenotype in curly birch (Betula pendula var. carelica (Mercklin) Hämet-Ahti). In addition of the figured wood, curly birches display reduced and aberrant growth, indicating that the causative locus (loci) is (are) vital for normal tree development. To explore the genetic basis of this mutation, we studied the inheritance of the curly birch phenotype in a progeny trial (crosses between curly birch parent trees, between curly and normal phenotypes, and from selfings of curly trees). Based on the external morphology, the phenotypes of 11-year-old progeny trees were scored as either curly or wild. Based on the phenotypic segregation ratios, we postulate a simple Mendelian inheritance model for curliness: (i) a one-locus, two-allele model in which the allele coding for curly phenotype is dominant over the allele coding for normal phenotype and (ii) the semi-dominant curly allele is lethal when homozygous. We expect that further studies on the molecular genetic basis of the curly birch phenotype will provide valuable information on the developmental pathways involved in wood formation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".