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Record W2595027241 · doi:10.1016/j.ijpp.2017.01.001

Stressing out in medieval Denmark: An investigation of dental enamel defects and age at death in two medieval Danish cemeteries

2017· article· en· W2595027241 on OpenAlexafffund
Julia A. Gamble, Jesper L. Boldsen, Robert D. Hoppa

Bibliographic record

VenueInternational Journal of Paleopathology · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of CanadaCanada Research Chairs
KeywordsDanishEnamel paintHistoryDentistryArchaeologyMedicinePhilosophy

Abstract

fetched live from OpenAlex

The influence of early life stress on later life experiences has become a major focus of research in medicine and more recently in bioarchaeology. Dental enamel, which preserves a record of childhood stress events, represents an important resource for this investigation when paired with the information from adult skeletal remains, such as age at death. The purpose of this research was to use a life history approach to the exploration of sex differences in the relationship between childhood stress and adult longevity by examining accentuated striae of Retzius (AS). A medieval Danish sample (n=70) drawn from the rural cemetery of Sejet and the urban cemetery of Ole Wormsgade was considered for AS and age at death. The results suggest sex differences in survivorship, with more stress being associated with reduced survivorship in males and increased survivorship in females. A consideration of AS formation time also suggests a difference in the impact of developmental timing between males and females. These results are interpreted in terms of differential frailty and selective mortality, drawing in both biomedical and cultural perspectives.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.071
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.059
GPT teacher head0.345
Teacher spread0.286 · 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 source (direct Gemma or distilled Codex), 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

Citations49
Published2017
Admission routes2
Has abstractyes

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