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Record W2963862802 · doi:10.1127/anthranz/2019/0966

Postnatal maturation of the sternum in a Portuguese skeletal sample: a variable ossification process

2019· article· en· W2963862802 on OpenAlexaff
Vanessa Campanacho, Andrew Chamberlain, Hugo F.V. Cardoso

Bibliographic record

VenueAnthropologischer Anzeiger · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsSimon Fraser University
FundersArts and Humanities Research Council
KeywordsSternumOssificationForensic anthropologySkeleton (computer programming)JuvenileSkullAnatomyMedicineBiologyGeography

Abstract

fetched live from OpenAlex

The timing of skeletal maturation is one of the common indicators used to estimate age at death of juvenile skeletal remains. Skeletal maturation of the sternum has received less attention than other anatomical locations, and there is a general lack of detailed information about the fusion timing in the dry sternum that can be used for the estimation of age. The objective of this study is to document the age variation in the fusion of the body sternebrae, and both clavicular and intercostal notches. A three stage scale scheme was used (unfused elements, partial, and complete fusion) to quantify fusion of primary and secondary ossification centres in a sample of 68 individuals of both sexes from the identified skeletal collection housed at the National Museum of Natural History and Science in Lisbon, Portugal. Analysis was performed only for the pooled sex sample due to small sample size. Wide age intervals were obtained for fusion stages at all of the sternal centres. Primary ossification centres start to fuse between 1 and 27 years of age, with sternebrae 3 and 4 completing their fusion first. Secondary ossification centres fuse between 5 to 25 years of age. Results reflect considerable variability among individuals in the maturation of the sternum.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.737
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.011
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.262
Teacher spread0.246 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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