A PCR Multiplex and Database for Forensic DNA Identification of Dogs
Bibliographic record
Abstract
Animal-derived trace evidence is a common finding at crime scenes and may provide an important link between victim(s) and suspect(s). A database of 558 dogs of pure and mixed breeds is described and analyzed with two PCR multiplexes of 17 microsatellites. Summary statistics (number of alleles, expected and observed heterozygosity and power of exclusion) are compared between breeds. Marked population substructure in dog breeds indicates significant inbreeding, and the use of a conservative theta value is recommended in likelihood calculations for determining the significance of a DNA match. Evidence is presented that the informativeness of the canine microsatellites, despite inbreeding, is comparable to the human CODIS loci. Two cases utilizing canine DNA typing, State of Washington v. Kenneth Leuluaialii and George Tuilefano and Crown v. Daniel McGowan, illustrate the potential of canine microsatellite markers for forensic investigations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".