Inbreeding in Japanese Quail Estimated by Pedigree and Microsatellite Analyses
Bibliographic record
Abstract
Accurately estimating inbreeding is important because inbreeding reduces fitness and production traits in populations. We analyzed information from pedigrees and from microsatellite markers to estimate inbreeding in a line of Japanese quail derived from a randombred line (QO) and maintained for 17 generations by pedigreed matings of brothers to groups of sisters. Pedigree data were used to calculate the inbreeding coefficient (F(IT)), which is the level of inbreeding based on a reference ancestor. Data from analysis of 14 microsatellite markers in the inbred and QO lines were used to calculate the population differentiation (F(ST)) of the lines caused by inbreeding. The F(IT) was then calculated as F(IT) = F(IS) + (1 - F(IS)) x F(ST), where F(IS) is the level of inbreeding in the inbred line. Observed heterozygosity from analysis of the microsatellite markers of the QO and inbred lines was 0.43 and 0.21, respectively, and the number of alleles was 3.29 and 1.93, demonstrating a reduction of genetic diversity in the inbred line. The F(IT) of the inbred line calculated from the pedigree and microsatellite marker analyses was 0.69 +/- 0.07 and 0.57 +/- 0.33, respectively. These data suggest that pedigree analysis was more accurate than microsatellite marker analyses for estimating inbreeding in this line of Japanese quail.
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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".