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Record W4401576621 · doi:10.3390/heritage7080206

Exploring Colour Palette in Pottery from Western Anatolia and East Asia—Colour Schemes to Inspire

2024· article· en· W4401576621 on OpenAlexfundno aff
A. Panagopoulou, Joanita Vroom, Anno Hein, Vassilis Kilikoglou

Bibliographic record

VenueHeritage · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Materials Analysis
Canadian institutionsnot available
FundersUniversity of British Columbia
KeywordsPalette (painting)PotteryEast AsiaGeographyAncient historyArchaeologyArtHistoryVisual artsChina

Abstract

fetched live from OpenAlex

In the present case study, the manufacturing technology for glazed pottery was investigated, with particular focus on the great variety of colours and glaze recipes used in Western Anatolia and East Asia and observed in finds from rescue excavation sites in Greece. An assemblage of 40 ceramic fragments dating from the Late Byzantine and Islamic to the Ottoman/Venetian periods was examined for their decoration, surface treatment, and production technology. The peculiarities of the colour recipes applied on the glazed pottery of different assumed origins of production were investigated, focusing on glaze technology and employing colourants. This was achieved by the use of an analytical workflow that considered the compositional details of pigments, slip coatings, and glazes. The chemical evaluation was carried out utilising X-Ray Fluorescence Spectroscopy (pXRF) and Scanning Electron Microscopy with Energy Dispersive X-Ray Spectroscopy (SEM-EDS). Raman Spectroscopy provided information about the compositional variation, and the microscopic examination via Optical Microscopy (OM) and Scanning Electron Microscopy (SEM-EDS) yielded information about the sample stratigraphy of the examined ceramic sections. Through a wide range of colour and glaze recipes, this study of glazed ceramics was able to define and express the essential elements of each pottery workshop’s perception of colour.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.136
GPT teacher head0.248
Teacher spread0.113 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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