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Record W2789287534 · doi:10.1111/1556-4029.13751

Estimating Sex Using Metric Analysis of the Scapula by Postmortem Computed Tomography

2018· article· en· W2789287534 on OpenAlexaff
Zabiullah Ali, Christopher Cox, Michala K. Stock, Eddy E. Zandee vanRilland, Ana Rubio, David R. Fowler

Bibliographic record

VenueJournal of Forensic Sciences · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsOffice of the Chief Medical Examiner
Fundersnot available
KeywordsDiscriminant function analysisLinear discriminant analysisMetric (unit)TriageDiscriminantForensic anthropologyComputed tomographyScapulaLogistic regressionArtificial intelligenceMathematicsPattern recognition (psychology)StatisticsMedicineComputer scienceRadiologySurgeryEngineeringGeographyMedical emergencyArchaeology

Abstract

fetched live from OpenAlex

Postmortem computed tomography (CT) has been extensively used in the last decade for identification purposes and in various anthropologic studies. Postmortem CT measurements of scapulae, analyzed using logistic discriminant function developed in this study, showed 94.5% accuracy in estimating sex. Data analyzed using the Dabbs and Moore-Jansen (2010) discriminant function and the discriminant function generated in this study provided nearly identical results with disagreement in only one case. Height and weight were not statically significant in sex prediction. The results of this study show that data obtained from volume rendered postmortem CT images can be considered reliable and treated as a practical option to standard anthropological methods, especially in mass fatalities as a rapid triage tool for sex determination.

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 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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.036
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 head0.042
GPT teacher head0.301
Teacher spread0.259 · 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 teacher head, not a consensus.

Study designObservational
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

Citations16
Published2018
Admission routes1
Has abstractyes

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