An Application of the CHAID Algorithm to Study the Environmental Impact of Visitors to the Teide National Park in Tenerife, Spain
Bibliographic record
Abstract
The significant and complex relationship between visitor numbers to a national park and the environment calls for appropriate policies to be adopted. This paper analyzes the relationship from the perspective of visitors to the Teide National Park (TNP) in Tenerife, aiming to establish strategies to reduce visitors' environmental impacts. This is particularly important as the TNP, with over 3,000,000 visitors in 2015, is the most visited park in Spain and one of the most visited in Europe. An empirical study was conducted during 2016 resulting in 805 valid questionnaires. A CHAID algorithm was then applied to segment visitors according to criterion variables. Findings show the first segmenting variable is transport type, with the car being the most frequently used by visitors. Specifically, the visitor segment coming by car is also associated with the longest stays in the TNP. Regarding the practical and social implications, it is assumed the longer the stay, the greater the environmental impact. These results highlight the need for new transport strategies for the park with improved, less polluting vehicles.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".