Impact of air pollution: tourists’ decision making behaviour during rural tourism
Bibliographic record
Abstract
In recent years, the increasingly severe air pollution has not only posed a threat to residents’ health but also had a significant influence on tourists’ travel decision making behaviours. This study utilised empirical dynamic model–long short-term memory model to predict air pollutant concentrations in Xidi Village, China, and investigated their impact on tourists’ travel decisions. According to the results, there is a negative correlation between air quality and air quality index (AQI). The lowest AQIs were recorded in May and August 2022, with visitor numbers reaching 12 305 and 11 705, respectively. During the period from May to October, except for ozone (O 3 ), which reached a maximum concentration of 157 μg/m 3 , all other pollutant concentrations remained at low levels. According to the predictions of the model, Xidi Village often experiences high concentrations of air pollutants during the spring and winter seasons, leading to hazy weather. This information provides accurate air quality data for tourists, helping them avoid periods of severe pollution at their travel destinations and reducing travel risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".