Application of microsatellite DNA markers to discriminate between maternal and genetic effects on scalation and behavior in multiply-sired garter snake litters
Bibliographic record
Abstract
Incomplete knowledge of pedigrees sometimes limits the methods of estimating quantitative genetic parameters (heritability, genetic correlation) in nature and may result in estimates that are inflated by nongenetic sources of variation. North American garter snakes and their allies provide a model system for investigating evolutionary quantitative genetics, but estimates of quantitative genetic parameters in these snakes are mostly based on offspring-dam regression and full-sib analysis, methods that fail to discriminate between maternal genetic, maternal environmental, and direct genetic effects on traits of interest. Using data from the garter snake Thamnophis sirtalis, we demonstrate that microsatellite DNA markers can be used to identify full-sib sireships within litters in species that produce large numbers of offspring and in which multiple paternity is common. This allows estimation of quantitative genetic parameters using a maternal half-sib analysis in which sires are nested within dams. Six microsatellite DNA loci were scored for four wild-caught dams and their 73 offspring and revealed two full-sib sireships within each litter. Maternal half-sib analyses of scalation and behavior suggest that heritability may be lower and maternal effects larger than was previously thought.
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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 | 0.000 |
| 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".