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Record W2001363923 · doi:10.1520/jfs2004206

A Comparative Reliability Analysis of Computer-Generated Bitemark Overlays

2005· article· en· W2001363923 on OpenAlexaff
AH McNamee, D Sweet, Iain Pretty

Bibliographic record

VenueJournal of Forensic Sciences · 2005
Typearticle
Languageen
FieldDentistry
TopicDental Research and COVID-19
Canadian institutionsUniversity of British Columbia
FundersNational Institute for Health and Care Research
KeywordsOverlayReliability (semiconductor)Adobe photoshopMedicineAdobeDentistryOrthodonticsStatisticsSoftwareComputer scienceMathematicsMultimedia

Abstract

fetched live from OpenAlex

This study compared the reliability of two methods used to produce computer-generated bitemark overlays with Adobe Photoshop (Adobe Systems Inc., San Jose, CA). Scanned images of twelve dental casts were sent to 30 examiners with different experience levels. Examiners were instructed to produce an overlay for each cast image based on the instructions provided for the two techniques. Measurements of the area and the x-y coordinate position of the biting edges of the anterior teeth were obtained using Scion Image software program (Scion Corporation, Frederick, MD) for each overlay. The inter- and intra-reliability assessment of the measurements was performed using an analysis of variance and calculation of reliability coefficients. The assessment of the area measurements showed significant variances seen in the examiner variable for both techniques resulting in low reliability coefficients. Conversely, the results for the positional measurements showed no significant differences in the variances between examiners with exceptionally high reliability coefficients. It was concluded that both techniques were reliable methods to produce bitemark overlays in assessing tooth position.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
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 head0.059
GPT teacher head0.381
Teacher spread0.323 · 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 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

Citations26
Published2005
Admission routes1
Has abstractyes

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