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Record W1629295276 · doi:10.1520/jfs15212j

The Efficiency of an X-Ray Screening System at a Mass Disaster

2002· article· en· W1629295276 on OpenAlexaff
Neville W. Goodman, LB Edelson

Bibliographic record

VenueJournal of Forensic Sciences · 2002
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsOffice of the Chief Medical Examiner
Fundersnot available
KeywordsPoison controlForensic engineeringMedical emergencyEngineeringMedicine

Abstract

fetched live from OpenAlex

This is a study to determine the efficiency and efficacy of using an X-ray security screening system to locate both dental fragments and other foreign objects that might be commingled with fragmented remains in a mass disaster. A controlled study by the Pennsylvania Dental Identification Team (PADIT) revealed that a manual examination of simulated body bags containing commingled dental parts and foreign objects by a team of trained forensic odontologists was very effective in locating dental fragments and in finding foreign objects. Although this was effective, it was not efficient, because it was very time consuming. With the use of an X-ray security screening system, the time factor could be reduced. This study also revealed that even though this sophisticated equipment could reduce the time factor in locating commingled dental and foreign objects, a forensic odontologist should be utilized to be most effective.

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.929
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.022
GPT teacher head0.286
Teacher spread0.264 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2002
Admission routes1
Has abstractyes

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