MétaCan
Menu
Back to cohort
Record W2558594663 · doi:10.5539/res.v9n1p1

Color Structure in the Persian Painting

2016· article· en· W2558594663 on OpenAlexvenueno aff
Zahra Pakzad

Bibliographic record

VenueReview of European Studies · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Materials Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPersianPaintingArtVisual artsLiteratureLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Written manuscripts and literary treatises are among the most important documents of knowledge on traditional color production techniques related to painting, and as they have survived thanks to desirable maintenance and preservation from the ancient times to the present time, they can be good sources for identifying and extracting traditional color production methods related to paining. Especially, the illustrated books simultaneously with their writing are an evidence of the contents presented in those manuscripts and treatises. Therefore, by an aim to identify and revive traditional color production techniques, the present descriptive-analytic research examines some of the available handwritten manuscripts and literary treatises. Then, with emphasis placed on the knowledge acquired and the modern facilities, some of the colors are made. The present study was performed by raising the major question that what ancient literary books are the sources of production colors used in Persian painting, and what were the nature of color production techniques and traditional color characteristics in the past. Thus, the study population includes Golestan Honar, Qanun al-Sovar, Majmoueh al-Sanaye’ and 14 other treatises relevant to this issue, and the data collection was performed in a library- and experimental-based manner. The result of this study was the extraction, preparation and remaking of seven main mineral colors in Persian painting.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.799
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.082
GPT teacher head0.287
Teacher spread0.205 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueReview of European StudiesSame topicCultural Heritage Materials AnalysisFrench-language works237,207