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Dental wear in non-adults of Late Bronze Age pastoralists from Middle Volga and Southern Ural regions

2024· article· en· W4408297291 on OpenAlexfundno aff
Marina K. Karapetian

Bibliographic record

VenueVestnik arheologii, antropologii i ètnografii · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsnot available
FundersRussian Science FoundationRoyal SocietyUniversity of Alberta
KeywordsBronze AgePastoralismVolga regionGeographyBronzeAncient historyArchaeologyHistoryLivestockForestry

Abstract

fetched live from OpenAlex

This study analyses dental wear of children and adolescents from the Late Bronze Age kurgans of the Mid-dle Volga and Southern Ural regions (N = 97). The rate of wear in this sample was compared with a Post-Medieval rural sample from Netherlands. A modified Smith’s scale was used, adapted for two sets of teeth. Wear scores were strongly correlated with age, both when analyzing groups of teeth separately and when scores were averaged for each individual. The studied Volga-Ural sample had a significantly higher rate of dental wear com-pared to the rural sample from the Netherlands, due to higher average scores between 7–14 years of age and lower scores below 7 years of age. The observed intersection of regression lines may be either due to biological or methodological causes. In general, there is some trend towards lower level of wear of deciduous teeth in the Volga-Ural sample compared to a few samples from the literature, which is consistent with the hypothesis of lower attrition rates in pastoralists. It is essential to expand comparative data using the same scoring technique, as well as to address a number of methodological issues related to the simultaneous analysis of two sets of teeth.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score1.000

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 studies0.0000.033
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.234
Teacher spread0.212 · 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 designQualitative
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

Citations0
Published2024
Admission routes1
Has abstractyes

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