Application Research of ip Image Design under the concept of Environmental Protection
Bibliographic record
Abstract
Wuhan enjoys the reputation of "the city of one hundred Lakes". The numerous and beautiful lake scenery is a beautiful scenery line of the city. According to the Wuhan Lake Protection Master Plan approved at the end of 2018, there are 166 natural lakes in Wuhan, and the water area of Wuhan accounts for a quarter of the city's total area. However, with the development of today's society, the pollution of lakes caused by sewage discharge and household garbage has become more and more serious, and the major lakes in Wuhan have been polluted to varying degrees. Therefore, it is more important for Wuhan, also known as "River City", to control the lake water area, and the form is more severe, which requires people to have a higher awareness of environmental protection. Secondly, in the development of Wuhan lake culture, the "cultural atmosphere" is insufficient, and many lake cultural values need to be developed. In today's era, IP construction, as a production mode of cultural system construction, is more easily accepted by the public. Taking IP image as a new carrier of culture, it is more likely to be entertaining and interesting in communication and thus more likely to bring more possibilities for the dissemination of public environmental awareness. In this situation, this paper tries to arouse the public's awareness of environmental protection in the form of IP from the perspective of Wuhan lake culture, using literature research, case analysis, design practice and other methods, and create a unique, malleable and social IP image series design.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".