Mountain bike tourism development under the midnight sun: Capitalizing on destination attributes to maximize tourism potential in the Yukon Territory, Canada.
Bibliographic record
Abstract
The Yukon Territory, in Canada's Arctic, has tremendous potential for mountain bike tourism development. The territory has abundant natural beauty an excellent trail system committed advocates at the small business, municipal, and territorial levels and a mystique that naturally draws people there. The Yukon has already established itself as an adventure tourism destination for activities such as paddling and dog-sledding. However, the mountain bike segment of the Yukon tourism industry remains very small. By investigating the destination attributes that draw riders to the Yukon, the local mountain bike tour operator industry and the broader mountain bike tourism community will be able to capitalize on an ever-expanding tourist market as other destinations have done. Destination attributes are defined as the natural and physically constructed characteristics of a specific location that would draw a tourist to travel to an area. The main findings of the study were that the three attributes that were highlighted in previous studies' scenery, trail quality, and trail variety were also the three most desired attributes in this study. In addition, participants indicated that they would like to experience a different trail each day that they are on a vacation. Finally, throughout the study it was highlighted that the uniqueness of a destination is very important to trip satisfaction. The underlying question is how the mountain bike tourism product, and its perception by the tourists that visit can be better understood the answer can provide insight to be capitalized on in order to grow the industry effectively. --Leaf 2.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".