Bibliographic record
Abstract
Domestic animals represent an extremely useful model for linking genotypic and phenotypic variation. One approach involves identifying allele frequency differences between populations, using F(ST), to detect selective sweeps. While simple to calculate, FST may generate false positives due to aspects of population history. This prompted the development of hapFLK, a metric that measures haplotype differentiation while accounting for the genetic relationship between populations. The focus of this paper was to apply hapFLK in sheep with available SNP50 genotypes. The hapFLK approach identified a known selective sweep on chromosome 10 with high precision. Further, five regions were identified centered on genes with strong evidence for positive selection (COL1A2, NCAPG, LCORL, and RXFP2). Estimation of global F(ST) revealed many more genomic regions, providing empirical data in support of published simulation-based results concerning elevated type I error associated with F(ST) when it is being used to characterize sweep regions. The findings, while conducted using sheep SNP data, are likely to be applicable across those domestic animal species that have undergone artificial selection for desirable phenotypic traits.
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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 | 0.002 | 0.002 |
| 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".