DNAc: A Clustering Method for Identifying Kinship Relations Between DNA Profiles Using a Novel Similarity Measure*
Bibliographic record
Abstract
After decades of refinement, DNA testing methods have become essential tools in forensic sciences. They are essentially based on likelihood ratio test principle, which is utilized specifically, by using as prior knowledge the allele frequencies in the population, to confirm or refute a given kinship hypothesis made on two genotypes. This makes these methods ill suited when allele frequencies or kinship hypotheses are unavailable. In this paper, we introduce DNAc, a new clustering methodology for DNA testing based on a new similarity measure that allows an accurate retrieval of the degree of relatedness among two or more genotypes, without relying on kinship hypotheses or allele frequencies in the population. We used DNAc in analyzing microsatellite DNA sequences distributed among 12 genotypes from normal individuals from two distinct families. The results show that DNAc accurately determines kinship among genotypes and further gathers them in the appropriate kinship groups.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".