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Record W1971004707 · doi:10.1002/ajpa.10376

Skeletal age, dental age, and the maturation of KNM‐WT 15000

2004· article· en· W1971004707 on OpenAlexaboutno aff
Shelley Smith

Bibliographic record

VenueAmerican Journal of Physical Anthropology · 2004
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHuman skeletonHomo erectusLongitudinal sampleGrowth spurtEnamel paintSkeleton (computer programming)BiologyAge groupsDemographyEvolutionary biologyMedicineDentistryPsychologyAnatomyDevelopmental psychologyEndocrinology

Abstract

fetched live from OpenAlex

The skeleton of the Homo erectus boy from West Lake Turkana, Kenya (KNM-WT 15000), is remarkably complete, and this individual has thus provided a case study for several researchers examining Homo erectus growth. Using data from a longitudinal study of Montreal French-Canadian children, it is shown that while dental and skeletal ages match reasonably well at the level of a sample of children, individuals can display differences between skeletal and dental ages of 2 years or more. Furthermore, the relationship between these two markers may change over time in individual children. It is also possible to find children with patterns of dental maturation similar to KNM-WT 15000's pattern in the Montreal sample. Therefore, neither the discrepancy between skeletal age and dental age alone nor the pattern of dental maturation as assessed by dental stages precludes a human-like pattern of growth, including an adolescent growth spurt, for this individual. Some indicators (e.g., estimated body size for predicted age, and enamel formation) do suggest possible growth-patterning differences from modern humans, and therefore earlier maturation is a reasonable hypothesis, but caution is warranted, given the large degree of modern human variation in developmental markers and the inherent uncertainty in precise estimation of KNM-WT 15000's maturational parameters.

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
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 score0.655
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.149
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 head0.010
GPT teacher head0.266
Teacher spread0.256 · 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

Citations41
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Physical AnthropologySame topicForensic Anthropology and Bioarchaeology StudiesFrench-language works237,207