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Record W2883395538 · doi:10.1002/oa.2691

The subtleties of stress: A comparative analysis of skeletal lesions between the Medieval and post‐Medieval Black Friars cemetery population (13th to 17th centuries)

2018· article· en· W2883395538 on OpenAlexaff
Amy B. Scott, Robert D. Hoppa

Bibliographic record

VenueInternational Journal of Osteoarchaeology · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsUniversity of ManitobaUniversity of New Brunswick
Fundersnot available
KeywordsOsteologyPaleopathologyPopulationBioarchaeologyStress (linguistics)DemographyGeographyArchaeologyMedicineSociologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract The study of stress from an osteological perspective is challenging as we use skeletal remains to explore the lived experience and patterns of health. As an intricate overlap of multiple biological processes, the stress response system guides our understanding of how and why stress manifests as it does. Using traditional osteological methods of stress analysis, specifically cribra orbitalia, porotic hyperostosis, and enamel hypoplastic lesions, this study focuses on the relationship between these indicators to explore differences in stress manifestation in the Medieval and post‐Medieval periods in Denmark. Using the Black Friars cemetery population (13th–17th centuries), results show an increase in stress from the Medieval into the post‐Medieval period likely dictated by the strains of urbanism on a predominantly poor population. Additionally, a younger mean age at death was noted when multiple mild–moderate indicators were present as compared with one severe indicator being present. A recognition of the intricacies of the stress response effectively aids in the exploration of stress manifestation and the relationship, if any, between these well‐used skeletal indicators.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.031
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 head0.045
GPT teacher head0.318
Teacher spread0.273 · 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 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

Citations12
Published2018
Admission routes1
Has abstractyes

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