Estimating the correlation of non-allele descents along chromosomes
Bibliographic record
Abstract
The pattern of the correlation of non-allele descents among linked sites is an important aspect for an insight into the genomic evolution at the population level. Here, we present a new statistical method for estimating two types of non-allele descent correlations. One is the standardized parental descent disequilibrium termed by Cockerham & Weir (1973), the other is the standardized disequilibrium between non-allele descent segments from the same chromosome. Essential to this analysis is the partitioning of the joint identity-by-state probability for a random pair of non-allele gametes into the different components of identity by descents at the two or three sites. We consider the samples of phased haplotypes of single nucleotide polymorphism (SNP) markers and the weighted least square method for fast parameter estimation. Monte Carlo simulations demonstrate that robustly unbiased estimates with appropriate precisions can be obtained with certain sample sizes, ~100 diploids, under the impacts of allele frequency distributions and linkage disequilibrium. This method can be used to construct the maps of non-allele descent correlation blocks for the population whose genetic pedigree is not required on a prior basis.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".