Geographical Elements Affecting Quaintness in Tourist Town Developments-Topography and Transportation
Bibliographic record
Abstract
An understanding of recreation geography is an important factor, now and in the future, as we strive to make the best decisions to serve the public. Although the roots of recreational geography extend into the 1920s in the United States,l there are few American articles devoted to the subject. Literature pertaining to the field is mainly from British, European, and Canadian sources. How transportation, topography, and planning affect tourist destination patterns is of utmost importance to recreation geographers. Prior studies 1-13 have not reached agreement on what methods may be used for determining the effects of transportation or topography on recreational areas or their influence on maintaining the character of the region. Gearing, Swart, and Var's model 2 used several factors in determining tourist attractiveness in Turkey, including climates, relief, and roads. Their test is to be used with caution, however, because for each region to which the test is applied, the independent variables will need to be redefined. Fesenmaier and Lieber3 also conducted a study to evaluate the reliability of outdoor research and concluded that human behavior is more consistent within a person's home region. Differences observed within the parameters of demand forecasting models reflected underlying regional variations. To these authors accessibility was a very important factor in determining the popularity of a region. Var, Beck, and Loftus4 predicted that sustained expansion of the travel industry over the next few years in British Columbia would occur, and that attractiveness could serve as a measure of popularity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".