An Examination on the Historical Distribution and Transformation of Cinnabar Localities Through Chinese Materia Medica Works
Bibliographic record
Abstract
The distribution and transformation of cinnabar localities in the history of China as reflected in Chinese materia medica works has been a dynamic process. In the Pre-Qin Period and Qin-Han Dynasties, mining clustered around the few cinnabar localities that were scattered. During Wei, Jin, and the Southern and Northern Dynasties, the number of cinnabar localities gradually increased, and there was a shift of production center. In Tang-Song Dynasties, localities containing cinnabar were more explicitly identified and significantly expanded in size; the tendency toward a shift of production center became more obvious. During Yuan, Ming, and Qing Dynasties, the size of cinnabar localities continued to expand a little. The increasing expansion of the localities and the gradual shift of production center was the result of the interplay of many factors including the medicinal attributes and functions of cinnabar, society’s demand for cinnabar, mining technologies, the attributes of cinnabar as a natural resource and its religious and cultural functions. An in-depth examination and understanding of the pattern of distribution and transformation of cinnabar localities through Chinese materia medica works would offer better guidance for present-day mining of cinnabar and selection of authentic herbal medicine.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".