Exploring Colour Palette in Pottery from Western Anatolia and East Asia—Colour Schemes to Inspire
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".