Fuzzy-cluster-analysis based evaluation of regional tourism resources and development countermeasures——A case study on Pingliang city
Bibliographic record
Abstract
The scientific appraises of tourism resources is the important basis for optimizing and rational development-plan of regional tourism resources.Taking Pingliang city of Gansu Province as example,using fuzzy cluster meghod and relevant knowledge,adoption quantity of all monomer,monomer density,type abundances,reserves abundances,average level and quantity of the best monomer quality as indexes,tourism resources of seven counties in Pingliang city were analyzed.The results indicated resources condition of Kongtong area stood highly at the first place.That in Jingchuan County,Chongxin County,Huating County,Zhuanglang County developed well.That in Lingtai county,Jingning county lagged comparatively.The conclusion was comparatively objective,perfecting the area cognition of regional tourism resources.Based on that some development strategies about exploiting tourist resources of Pingliang city were put forward so as to offer some scientific evidences for the rational exploitation of tourist resources and thereby promote the tourism industry sustainable development.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".