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Record W2060430607 · doi:10.1111/1556-4029.12380

A Test of Hartnett's Revisions to the Pubic Symphysis and Fourth Rib Methods on a Modern Sample

2014· article· en· W2060430607 on OpenAlexaff
Catherine E. Merritt

Bibliographic record

VenueJournal of Forensic Sciences · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPubic symphysisBone ageForensic anthropologyDemographyPopulationCorrectnessMathematicsStatisticsOrthodonticsAlgorithmMedicineSurgeryGeographyArchaeologyAnatomySociology

Abstract

fetched live from OpenAlex

Estimating age at death is one of the most important aspects of creating a biological profile. Most adult age estimation methods were developed on North American skeletal collections from the early to mid-20th century, and their applicability to modern populations has been questioned. In 2010, Hartnett used a modern skeletal collection from the Maricopia County Forensic Science Centre to revise the Suchey-Brooks pubic symphysis method and the İşcan et al. fourth rib methods. The current study tests Hartnett's revised methods as well as the original Suchey-Brooks and İşcan et al. methods on a modern sample from the William Bass Skeletal Collection (N = 313, mean age = 58.5, range 19-92). Results show that the Suchey-Brooks and İşcan et al. methods assign individuals to the correct phase 70.8% and 57.5% of the time compared with Hartnett's revised methods at 58.1% and 29.7%, respectively, with correctness scores based on one standard deviation of the mean rather than the entire age range. Accuracy and bias scores are significantly improved for Hartnett's revised pubic symphysis method and marginally better for Hartnett's revised fourth rib method, suggesting that the revised mean ages at death of Hartnett's phases better reflect this modern population. Overall, both Hartnett's revised methods are reliable age estimation methods. For the pubic symphysis, there are significant improvements in accuracy and bias scores, especially for older individuals; however, for the fourth rib, the results are comparable to the original İşcan et al. methods, with some improvement for older individuals.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.013
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 head0.058
GPT teacher head0.343
Teacher spread0.284 · 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 designTheoretical or conceptual
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

Citations30
Published2014
Admission routes1
Has abstractyes

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