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Record W2552548260 · doi:10.5617/viking.3911

Liv og død i Hierapolis Norske utgravninger i en hellenistisk–romersk–bysantinsk by i Lilleasia

2016· article· en· W2552548260 on OpenAlexaff
Rasmus J. Brandt, Sven Ahrens, Camilla Cecilie Wenn, Erika Hagelberg, G. Bjørnstad, K. Bortheim, Henrike Kiesewetter, Hannah Russ, E. Cappelletto, Hallvard R. Indgjerd, Megan Wong, Michael P. Richards, Irene Selsvold, Dorothy Kent Hill, Anne Nyquist

Bibliographic record

VenueViking · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicArchaeology and Historical Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsByzantine architecturePeriod (music)ArchaeologyAncient historyHistoryNorwegianOrnamentsOsteologyHellenistic periodYardGeographyArt

Abstract

fetched live from OpenAlex

Life and Death at Hierapolis. Norwegian excavations in a Hellenistic–Roman–Byzantine town in Asia Minor From 2007 to 2015 the University of Oslo, invited by The Italian Archaeological Mission at Hierapolis in Phrygia, conducted archaeological research in the North-East Necropolis at Hierapolis. The aim of the project was to document all visible tombs and sarcophagi of the necropolis and excavate selected tomb areas and tombs. The research, including osteological, DNA- and isotope-analyses, investigated the life of the inhabitants over a long period of time (ca. 100–1300 A.C.) with reference to tomb architecture, landscape perception, organization, entrepreneurship, ritual practices, genetic relations and origins, demography, health, sickness, diets, and individual movement patterns. Many of the aims are answered in the article, here shall only be mentioned two important discoveries: cremation has been documented as late as the 5th/6th centuries A.C., in periods of crisis perhaps used to signal pagan opposition to imposed Christian practices; the life conditions in the Roman/Early Byzantine period were much better than in that of the Middle Byzantine period.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.791
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.015
GPT teacher head0.205
Teacher spread0.190 · 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 designNot applicable
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

Citations1
Published2016
Admission routes1
Has abstractyes

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