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Record W4223653421 · doi:10.1038/s41598-022-08669-0

Recipes of Ancient Egyptian kohls more diverse than previously thought

2022· article· en· W4223653421 on OpenAlexaff
Marabel Riesmeier, Jennifer Keute, Margaret-Ashley Veall, Daniel Borschneck, Alice Stevenson, Anna Garnett, Alice Williams, Maria Ragan, Thibaut Devièse

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Materials Analysis
Canadian institutionsCanadian Cardiovascular Society
FundersArts and Humanities Research Council
KeywordsAncient egyptHistoryDiversity (politics)Environmental ethicsAncient historySociologyAnthropologyPhilosophy

Abstract

fetched live from OpenAlex

Kohl, a dark eye cosmetic, is a well-known part of Ancient Egyptian culture. Modern chemical analyses of kohls have largely found lead-based inorganic constituents, whereas earlier studies argued for a much broader range of constituents. Furthermore, organic materials in kohls remain severely understudied. This raises questions regarding the true diversity of materials and recipes used to produce kohls. We analysed the contents of 11 kohl containers from the Petrie Museum collection in London. The objects selected cover a broad range of times and locations in Egypt. Our multi-analytical approach allowed us to characterise both inorganic and organic components. Our data show that inorganic ingredients in kohl recipes are not only lead-based but also manganese- and silicon-based. Our analyses also revealed that organic ingredients derived from both plant and animal sources were commonly used in kohl recipes and sometimes even represent the main constituent. All these findings point towards more varied recipes than initially thought and significantly shift our understanding of Ancient Egyptian kohls.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.036
GPT teacher head0.240
Teacher spread0.204 · 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

Citations20
Published2022
Admission routes1
Has abstractyes

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