An Exponential Dispersion Model for the Distribution of Human Single Nucleotide Polymorphisms
Bibliographic record
Abstract
An analysis of 1.42 million human single nucleotide polymorphisms (SNPs), mapped by the International SNP Map Working Group, revealed an apparent power function relationship between the estimated variance and mean number of SNPs per sample bin. This relationship could be explained by the assumption that a scale invariant Poisson gamma (PG) exponential dispersion model could describe the distribution of SNPs within the bins. In this model the sample bins would contain random (Poisson distributed) numbers of identical by descent genomic segments, each with independently distributed and gamma distributed numbers of SNPs. This model was both qualitatively and quantitatively consistent with the conventional coalescent model. It agreed with the empirical cumulative distribution functions derived from the SNP maps as well as with simulated data. The model was used to estimate the heterozygosity pi, and the mean number and size of haplotype blocks for each chromosome. These estimates were consistent with measurements from conventional studies. This PG model thus provides an alternative to Monte Carlo simulation for description of the distribution of SNPs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".