MétaCan
Menu
Back to cohort
Record W2047864865 · doi:10.1520/jfs2004207

A PCR Multiplex and Database for Forensic DNA Identification of Dogs

2005· article· en· W2047864865 on OpenAlex
Joel Halverson, Christopher Basten

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Forensic Sciences · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsMicrosatelliteInbreedingDNA profilingBiologyPopulationLoss of heterozygosityMultiplexGeneticsForensic identificationForensic scienceSuspectEvolutionary biologyDatabaseAlleleDNAMedicineCriminologyComputer sciencePsychologyGene

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.230

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.318
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it