Status Analysis for Ganzi Tibetan Autonomous Prefecture Baiyu County Hepo Tibetan Nationality Metal Forging Technology
Bibliographic record
Abstract
Along with the fast growing of social economy and the continuous improvement of people’s living conditions, more and more people begin to focus on traditional culture and traditional handicrafts, which are the historic products. And also people focus on intangible cultural heritage, because it is provided with a certain value and research sense. The paper is studying Ganzi Tibetan Autonomous Prefecture Baiyu County Hepo Tibetan Nationality Metal Forging Technology, which also boasts its historic and cultural value and aesthetic and collection value and so on. Because of geographical conditions and the uniqueness of ethnic characteristics, the exquisite technology has been reserved until now. While this kind of technology relying on unique inheritance way also faces a situation that the technology may be disappeared and the inheritors may be lost at any time. Hence, it is necessary to study and investigate Hepo Tibetan Nationality metal forging technology; this may do a little help to the inheriting of the Hepo Tibetan Nationality. Meanwhile, as an intangible cultural heritage, the studying on forging technology can provide supplement for the protection and development of intangible cultural heritages in documents, so as to focus more attentions from scholars. Through field visit and seeing the elegant and exquisite Hepo metal forged art crafts, we will be so impressive, and this is not only the aesthetic enjoying in art crafts, more value is derived from its contains in cultural and handicraftsmen’ spirit.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".