Bibliographic record
Abstract
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".