Investigating the Effective Bioclimatic Factors on Tourism Industry (Case of Study: Zanjan, Iran)
Bibliographic record
Abstract
The tourism industry is one of the largest and fastest growing economic factors in the contemporary worlds. Many factors affect the tourism industry; one of the most important of them is climate. Unfortunately, tourism literature has not paid much attention to the effect of climatic factors on the industry as it worth. Therefore, in order to develop this area of global economic, it is necessary to recognize the capabilities and limitations of the climate area. In this research, in order to evaluate environmental conditions in there, indicators of effective temperature (ET), temperature-humidity (THI), Baker Index (CP), and physiological stress indicators, (Pphs) by using monthly statistic parameters of temperature, relative humidity wind and synoptic sampling stations during the period 2005 – 1955 are used. Results show that based on the parameters of ET, the maximum temperature in April, the minimum temperature in July and August and the average temperature can be seen in May. About THI index comfort conditions can be seen just in March and November and CP index indicates total bioclimatic comfort in summer. Index) also found that only the months of June, September and has been neutral in terms of biological stress.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".