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Record W7051578793

A paleopathological analysis of juveniles from Thebes: Childhood health in Byzantine Greece

2024· dissertation· en· W7051578793 on OpenAlexaff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2024
Typedissertation
Languageen
FieldEngineering
TopicElectrostatic Discharge in Electronics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsByzantine architectureJuvenileBioarchaeologyEarly childhoodPaleopathology
DOInot available

Abstract

fetched live from OpenAlex

Limited publications address juvenile health in Byzantine Thebes, Greece. As vulnerable and abundant members of most populations, children are essential to understanding the health experiences of past populations. This thesis examines juvenile skeletal material recovered from a Byzantine period cemetery (5th-9th centuries CE) located on Ismenion Hill in Thebes, Greece. This site is located adjacent to the early Christian church of St. Luke the Evangelist. In antiquity, St. Luke was traditionally considered to be a physician. Previous analyses of the adult skeletal sample revealed a high prevalence of leprosy, cancer, and infection, leading researchers to hypothesize a relationship between the cemetery and the church of the physician saint. This thesis investigates this hypothesis with respect to the juveniles recovered at this site. Eleven graves were studied and a minimum of 59 juvenile individuals were identified. Perinates (n=15), infants (n=9), and young juveniles (n=25) make up the majority of the sample, followed by older juveniles (n=8) and adolescents (n=2). Overall, 16 (27%) juveniles present with dental and/or skeletal pathologies. Through the process of differential diagnosis, infection, metabolic conditions, and hematopoietic disorders were found to dominate the sample. To understand why children were brought to and buried at Ismenion Hill, cultural, social, and environmental factors shaping childhood health in late antiquity are addressed.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.197
Teacher spread0.192 · 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

Explore more

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