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Record W2094155549 · doi:10.1520/jfs2003389

Recognition of Cemetery Remains in A Forensic Context

2004· article· en· W2094155549 on OpenAlexaffabout
T. Rogers

Bibliographic record

VenueJournal of Forensic Sciences · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForensic Entomology and Diptera Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsForensic scienceContext (archaeology)CriminologyForensic engineeringPsychologyArchaeologyHistoryEngineering

Abstract

fetched live from OpenAlex

The purpose of this paper is to provide guidelines for the recognition of human remains from modem and historic cemeteries found in a forensic context. Three avenues of evidence may be pursued to confirm the origin of cemetery remains: context, condition of the body, and associated artifacts. This article outlines types of North American cemeteries, demonstrating how land use over time has resulted in many being closed, moved, or forgotten, leaving only the context to indicate their presence. The condition of human cemetery remains varies considerably depending on cultural practices and burial environment, but many exhibit combinations of the following traits: dried or embalmed tissue; erosion of bony pressure points; cortical bone flaking; and bone damage due to autopsy or embalming. Examples of artifact types useful in recognizing cemetery remains are also provided. Two cases from British Columbia, Canada are presented to demonstrate the diagnostic features of a disturbed cemetery burial.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.002
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.073
GPT teacher head0.261
Teacher spread0.188 · 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 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

Citations28
Published2004
Admission routes2
Has abstractyes

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