The Determination of Sea Tourism Season According to the Climatical Conditions in Marmaris-Alanya Coastal Belt (SW of Turkey)
Bibliographic record
Abstract
Introduction Weather and climate, together with some other natural resources, such as geographical location, orography and landscape, play important roles for and recreation (de Freitas, 2003). elements, having the greatest influence on tourism, are temperature, sunshine radiation, precipitation, wind, humidity and fog (from Stern et al. 2000; Hamilton and Lau, 2004; Gomez Martin, 2005; Matzarakis, 2001, Cengiz et al, 2008) Tourism is one of the world's largest, fastest growing and most climate-dependent economic sectors (Wall, 2007). is influential for international tourism, which being situation-specific is responsive to its variability and change. One of the major attributes of the most tourist destinations is seasonality. Not only is there a regular round of activities associated with the seasons, there is also variation in activity in areas lacking a marked seasonal climate. This is because seasonal variations in visitation to areas of supply. Thus, for example, the desire for many Canadians to escape the Canadian winter to warmer climates creates a seasonal demand in temperate and tropical areas which do not have the same degree of annual variation in temperature (Wall, 2007). Maddison (2001) investigated the importance of climate as a determinant of the destination choice made by British tourists (Corobov, 2007). Various places in the world have a and weather and climate set limits. For example, administrators do not promote places with a little potential or appeal, as this would not be profitable. On the other hand, the tourist who chooses to visit such places would suffer inconvenience discomfort. Rainy summers or less snowy winters can have significant impacts on tourism (de Freitas, 2001). Climatic information can be useful in decision-making if presented in an appropriate form. Therefore it is important to identify which climate-related criteria people use to make their decisions about holiday destinations, taking into account that the human response to climate depends on individual perception and sensitivity (de Freitas, 2001). Favourable climate and weather conditions are essential advantages for recreational and activity. However, in temperate climatic belt they are characterised by seasonality. Tourism is highly dependent on climate. Climate factors, such as temperature, wind and sunshine, account for a large share of the succes of major regions, such as the Mediterranean (Amelung and Viner, 2007). The research dealing with climate relationships should consider three categories of information (de Freitas, 2003). These are aesthetic factors (cloudness, visibility, sunshine duration, day length), physical state of the atmosphere (precipitation, snow cower, wind, solar radiation, UV radiation, air pollution) and bio-thermal conditions (human heat balance considerations). to this concept, the actual weather is one of the basic demand indicators of recreational and potentials of any time, season and/or region (Blazejczyk, 2007). According to de (2001), there are two further aspects of climate that are relevant to tourism: first, there is the physical aspect. Here, the climate facilitates or hinders certain tourist activities whether through rain, wind or snow. For example, wind and rain will make a day of sunbathing at the beach impossible. Second, there is the aesthetic aspect of climate. This may be through the quality of light that affects the appearance of the tourists' surroundings or it may come from the appearance of the sky and of the sea and other water bodies. In the long run, climate has an effect on the other elements that fall under the aesthetic category of de Freitas (Hamilton, 2007). data must be presented in a form that relates to the individual's response to the weather or climate conditions. …
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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 | 0.002 | 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 | 0.000 |
| 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".