A study on the structure features and spatial and temporal dynamic mode of overseas tourists in Xi'an.
Bibliographic record
Abstract
After a survey of overseas tourists in Xi'an, the temporal and spatial change database of the overseas tourists in Xi'an is established with the help of the software Visual FoxPro. By statistical analysis, the overseas tourist market structure features, the temporal development rule and the tourist spatial flows between Xi'an and other 11 hot tourist cities are discussed . Then, some conclusions can be obtained: (1)The overseas tourists to Xi'an mainly come from 8 countries, i.2.Japan, The Republic of Korea, Germany, France, England, USA, Canada, and Australia; the overseas tourists to Xi'an are dominated by middle aged and young people with sightseeing as their tourist purpose, the tourist products are simplistic, and the income from the entertainment service is low. (2)The overseas tourists to Xi'an enter mainly from the ports of Beijiang, Shanghai, and Guangzhou. (3)The overseas tourists to Xi'an mainly pass through Beijing, Shanghai, Guangzhou, and Shenzjen, then to Beijing, Shanghai, and Guilin. (4)The annual overseas tourist flow between Xi'an and other 11 tourist hot cities is dominated by that of Beijing-Xi'an, then Shanghai-Xi'an, Xi'an-Shanghai, Guangzhou-Xi'an, Xi'an-Beijing, Xi'an-Guilin, Shenzhen-Xi'an. (5)The two way flow of overseas tourists between Xi'an and other 11 tourist hot cities is in an obvious imbalance state.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".