Algorithm Optimization and Innovative Design of Digital Inheritance System for Miao Family Weaving Handicraft in South Sichuan Province
Bibliographic record
Abstract
With the maturity of digital display technology, its application scope is also more and more extensive, and there are more and more application cases in the protection and inheritance of minority hand-weaving skills.This paper builds a general framework for the design of the digital inheritance system for the handloom weaving techniques of the Miao family in southern Sichuan, and applies three-dimensional modeling technology, three-dimensional animation technology, digital imaging technology and interactive interface design to complete the preliminary establishment of the digital display system for the handloom weaving techniques of the Miao people in southern Sichuan.Combined with the information dissemination characteristics of mobile intelligent terminals, relevant improvement programs are proposed.At the same time, the optimization and improvement of the digital display system is further improved to meet the needs of users.Comparing the users' experience and perception of the digital display system, the system designed in this paper is superior to R-Space in terms of functional scope and technology, and the average score of the system designed in this paper is 4.193, which is higher than the score of 3.985 of the R-Space system, and the system designed in this paper has a higher score.At the same time, the user's satisfaction with the system's interactivity is more stable in the three aspects of login start, system home page, and Chuannan humanities resources.In the login start, the scores of very satisfied, more satisfied, and general are 2, 3, and 2.5 respectively, which indicates that the user's experience of this paper's system is better.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".