Loess Tourism Resource Exploitation Strategy in the Chinese Loess Plateau: A Case Study of White Deer Plateau
Bibliographic record
Abstract
As one of the important geologic tourism resources in the world, the Chinese loess landscape tourism has not beendeveloped deeply enough, which can not fulfill tourists’ tourism demands on different levels. Based on many successfuldevelopment experiences about sand tourism and ice snow tourism, according to the feature of loess environment,geographic conditions and traffic advantage, combining the advantages, features and development actuality of the loesslandscape on the Chinese Loess Plateau, the idea of constructing the loess sculpture garden on the White Deer Plateau isproposed in the article. The main problems existing in the tourism development of the White Deer Plateau are analyzed,and the necessity and feasibility to construct the loess sculpture garden are demonstrated, and the main contents toestablish the loess sculpture garden in the White Deer Plateau are proposed. The loess sculpture garden can completelyshow the special natural landscapes and the humanistic landscapes in the Loess Plateau, enhance the landscape value ofthe loess landscapes, deeply dig the scientific and educational values, closely combining the landscape feature with thepopular science oriented feature, drive the development of the tourism of the White Deer Plateau and make it to becomethe hot landscape and the popular science base of the tourism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".