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Personal Identification of Cold Case Remains Through Combined Contribution from Anthropological, mt<scp>DNA</scp>, and Bomb‐Pulse Dating Analyses

2012· article· en· W2047847243 on OpenAlexaffabout
Camilla Speller, Kirsty L. Spalding, Bruce A. Buchholz, Dean P. Hildebrand, Jason Moore, Rolf W. Mathewes, Mark Skinner, Dongya Yang

Bibliographic record

VenueJournal of Forensic Sciences · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicForensic and Genetic Research
Canadian institutionsBurnaby HospitalBritish Columbia Institute of TechnologySimon Fraser University
FundersNational Institute of General Medical SciencesLawrence Livermore National LaboratoryU.S. Department of Energy
KeywordsRadiocarbon datingForensic scienceMitochondrial DNAForensic anthropologyArchaeologyHistoryBiologyGenetics

Abstract

fetched live from OpenAlex

In 1968, a child's cranium was recovered from the banks of a northern Canadian river and held in a trust until the "cold case" was reopened in 2005. The cranium underwent reanalysis at the Centre for Forensic Research, Simon Fraser University, using recently developed anthropological analysis, "bomb-pulse" radiocarbon analysis, and forensic DNA techniques. Craniometrics, skeletal ossification, and dental formation indicated an age-at-death of 4.4 ± 1 year. Radiocarbon analysis of enamel from two teeth indicated a year of birth between 1958 and 1962. Forensic DNA analysis indicated the child was a male, and the obtained mitochondrial profile matched a living maternal relative to the presumed missing child. These multidisciplinary analyses resulted in a legal identification 41 years after the discovery of the remains, highlighting the enormous potential of combining radiocarbon analysis with anthropological and mtDNA analyses in producing confident personal identifications for forensic cold cases dating to within the last 60 years.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.064
GPT teacher head0.383
Teacher spread0.319 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2012
Admission routes2
Has abstractyes

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