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Record W2910257446 · doi:10.1126/sciadv.aau7126

Medieval women’s early involvement in manuscript production suggested by lapis lazuli identification in dental calculus

2019· article· en· W2910257446 on OpenAlexaff
Anita Radini, Monica Tromp, Alison I. Beach, Estella P.S. Tong, Camilla Speller, Michael McCormick, John Dudgeon, Matthew J. Collins, Frank Rühli, Christina Warinner

Bibliographic record

VenueScience Advances · 2019
Typearticle
Languageen
FieldComputer Science
TopicLibrary Science and Information Systems
Canadian institutionsUniversity of British Columbia
FundersEngineering and Physical Sciences Research CouncilLeverhulme TrustMäxi-StiftungMax-Planck-GesellschaftNational Science Foundation
KeywordsIdentification (biology)Calculus (dental)DentistryBiologyMedicineBotany

Abstract

fetched live from OpenAlex

During the European Middle Ages, the opening of long-distance Asian trade routes introduced exotic goods, including ultramarine, a brilliant blue pigment produced from lapis lazuli stone mined only in Afghanistan. Rare and as expensive as gold, this pigment transformed the European color palette, but little is known about its early trade or use. Here, we report the discovery of lapis lazuli pigment preserved in the dental calculus of a religious woman in Germany radiocarbon-dated to the 11th or early 12th century. The early use of this pigment by a religious woman challenges widespread assumptions about its limited availability in medieval Europe and the gendered production of illuminated texts.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.008
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.009
GPT teacher head0.235
Teacher spread0.226 · 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

Citations78
Published2019
Admission routes1
Has abstractyes

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